Monitoring renal function and adjusting dosing for patients with impaired renal function are not required with denosumab (60 mg every 6 months). However, these patients have an increased risk for developing hypocalcemia. This case report describes a patient with renal impairment who developed severe hypocalcemia after receiving denosumab. Approval in this setting was based on the Fracture Reduction Evaluation of Denosumab in Osteoporosis Every 6 months (FREEDOM) trial, which randomized women aged 60–90 years with postmenopausal osteoporosis  to receive denosumab (60 mg every 6 months) or placebo for 36 months. The trial showed that denosumab significantly reduced the risk of fracture 2. In the oncology setting, denosumab  is approved in the United States and Europe for use in patients with bone metastases from solid tumors, but not in patients with multiple myeloma3.A new antiresorptive therapy, denosumab , is approved in the United States and Europe for the treatment of women with postmenopausal osteoporosis who are at high risk for fracturen = 73; creatinine clearance [CrCl] 15–29 mL/min) or impaired renal function  compared with those with normal renal function 4. Thus, the label for denosumab (60 mg every 6 months) does not require monitoring renal function prior to administration and only states that patients with CrCl < 30 mL/min or receiving dialysis are at increased risk for hypocalcemia1.Denosumab is a fully human monoclonal antibody against receptor activator of nuclear factor κB ligand (RANKL), a key mediator of bone remodeling through the inhibition of osteoclast activity. Subset analyses of the FREEDOM trial showed that denosumab (60 mg every 6 months) was not associated with an increase in adverse events among patients with severe renal impairment  compared with alendronate in postmenopausal women with osteoporosis2, the denosumab label states that severe hypocalcemia can occur in patients receiving denosumab1. Furthermore, because of an imbalance in the number of serious infections and dermatologic adverse events in the FREEDOM trial and the increased risk for developing hypocalcemia, the US Food and Drug Administration required a risk evaluation and mitigation strategy (REMS) for denosumab9. The REMS includes a medication guide that lists hypocalcemia as a side-effect and warns that this condition is often asymptomatic, but does not suggest routine monitoring of calcium levels9.Bisphosphonates, another class of antiresorptive agent approved for the treatment of postmenopausal osteoporosis, are not indicated for patients with severe renal impairment consider monitoring calcium levels1.Untreated hypocalcemia may lead to chronic conditions such as cataract formation, prolonged QT interval, hypotension, congestive heart failure, seizures, or dementia. To minimize the risk for developing hypocalcemia, it is suggested that patients receiving antiresorptive therapy also receive daily calcium and vitamin D supplements. The denosumab label calls for concomitant calcium (1000 mg) and vitamin D (at least 400 mg), but monitoring of calcium levels prior to or during therapy is not required. Furthermore, although the denosumab label cautions that patients with severe renal impairment  are at risk for hypocalcemia, the only guidance provided by the label for use in this patient population is to supplement with calcium and vitamin D and to The importance of monitoring calcium levels in patients with renal impairment is highlighted by an individual case of a 68-year-old woman with renal impairment who developed severe hypocalcemia after receiving a single 60-mg dose of denosumab. Patient comorbidities included chronic obstructive pulmonary disease, hypertension, osteoporosis, depression, rheumatoid arthritis, polycystic kidney disease, and chronic renal insufficiency. The patient was not able to tolerate alendronate, an oral bisphosphonate prescribed by her private medical doctor, which was discontinued on April 19, 2010, by Dr T. If an oral agent is not well-tolerated, both intravenous and subcutaneous routes of administration for bone supportive care agents are feasible options. Denosumab (60 mg every 6 months) was chosen over the intravenous bisphosphonate zoledronic acid because of the patient’s poor renal function. On October 13, 2010, denosumab was given per label to this patient with chronic renal insufficiency and no symptoms of hypocalcemia. Calcium (8.9 mg/dL) and serum creatinine (2.7 mg/dL) levels were last checked on June 2, 2010, 4 months prior to receiving denosumab. Other relevant laboratory values from this date included albumin (3.8 g/dL), alkaline phosphatase (47 U/L), total bilirubin (0.3 mg/dL), BUN (48 mg/dL), glucose (105 mg/dL), sodium (140 µg/L), potassium (4.7 µg/L), and chloride (109 µg/L).Eleven days after denosumab administration , the patient presented at the hospital with fever and chills for 1–2 days, productive yellow cough with clear lungs and no shortness of breath, swelling, generalized pain and tenderness, mild confusion, and dyskinesia . The patient was admitted to the hospital and was diagnosed with severe hypocalcemia . Thyroid hormone T4 (4.2 µg/dL), thyroid stimulating hormone , and vitamin D 1,25-dihydroxy (48 ng/mL) levels were normal, but parathyroid hormone (PTH) was high (409 pg/mL). Other relevant laboratory values included serum creatinine (2.23 mg/dL), albumin (2.1 g/dL), alkaline phosphatase (41 U/L), BUN (27 mg/dL), glucose (94 mg/dL), sodium (141 µg/L), potassium (4.5 µg/L), and chloride (116 µg/L). Blood calcium levels remained low (7.2 mg/dL) through October 28, 2010. With treatment , the patient’s blood calcium returned to near normal (8.3 mg/dL) 3 weeks later .10. Therefore, antiresorptive therapy-mediated inhibition of bone resorption can lead to lower serum calcium levels and secondary hyperparathyroidism10, which could contribute to hypocalcemia, especially in individuals deficient in serum vitamin D or PTH12. Finally, renal insufficiency can lead to impaired conversion of vitamin D to its active metabolite  and also may be a contributing factor to hypocalcemia13.Antiresorptive therapies inhibit bone resorption, which can reduce serum calcium levels in both normal and hypercalcemic individuals. Normal serum calcium levels are influenced by the effect of vitamin D 1,25-dihydroxy and PTH on calcium absorption, urinary calcium excretion, and bone remodeling activity in the skeleton 14. However, hypocalcemia has been reported in clinical trials of patients with cancer receiving antiresorptive therapies for metastatic bone disease. In patients receiving bisphosphonates  in this setting, the incidence of grade 3/4 hypocalcemia typically is not reported because of its low frequency and similar incidence with placebo15. Indeed, hypocalcemia was either not reported or was reported as an uncommon adverse event in clinical trials with bisphosphonates 15–29. In contrast with the bisphosphonate trials, results from recent phase III clinical trials in patients with advanced cancer reported more frequent hypocalcemia with denosumab versus ZOL . Furthermore, severe hypocalcemia was reported more often in patients receiving denosumab compared with ZOL 3.Hypocalcemia has not been reported with antiresorptive therapies  in clinical studies of osteoporosisThis case suggests that guidelines recommending a modified dosing schedule of denosumab  may be beneficial for patients with renal impairment, not because of nephrotoxic effects of the drug, but to avoid severe hypocalcemia. Furthermore, the case highlights the importance of monitoring calcium levels and renal function before and during denosumab therapy in patients with multiple comorbidities. Indeed, without monitoring renal function, how is a clinician to determine if a patient is at increased risk for developing hypocalcemia? Patients benefit from careful monitoring regardless of the route of administration  of antiresorptive therapies, and renal monitoring has positive benefits, particularly for patients with multiple comorbidities.


Various growth characters, namely, plant height, leaf area index, dry matter accumulation, number of days to different phenological stages, and yield, and yield contributing characters namely, cob length, number of grains cob−1, cob diameter (cm), and 100-seed weight (g), were significantly higher with S2 over S1 during both the years of experimentation. Fertilizer levels F3 (90 : 60 : 40) and F2 (75 : 50 : 30) at par with one another produced significant increase in growth and yield characters, namely, plant height, leaf area index, dry matter production at different growth stages, cob length, number of cobs plant−1, number of grains cob−1, and 100-seed weight over F1 (60 : 40 : 20). Significantly higher grain yield was recorded with fertilizer level F3 (90 : 60 : 40) being at par with F2 (75 : 50 : 30) and showed significant increase over F1 (60 : 40 : 20) with superiority of 5.4 and 5.7 per cent during 2011 and 2012, respectively. The findings of the study concluded that ridge method of sowing of maize with NPK levels of 75 : 50 : 30 kg ha−1 showed better performance of crop in terms of growth, yield, and yield attributes.To investigate the response of rainfed maize to sowing methods and NPK levels, an experiment was undertaken during kharif of 2011 and 2012 at Dryland (Kerawa) Agriculture Research Station, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Budgam. The experiment was laid out in a randomized block design with combination of 2 sowing methods  and 3 fertility levels (60 : 40 : 20, 75 : 50 : 30, and 90 : 60 : 40 N : P Among the modern agromanagement practices sowing method and fertilizer application are imperative for boosting the growth and production of maize especially under rainfed conditions. Considerable work has been reported on these aspects but efforts are still required to improve these techniques for getting maximum yield. Planting technique has a great role to play in increasing maize yield. Our farmers generally use the old broadcast method of sowing that has so many disadvantages, that is, uneven distributions of seeds, depth, and seed lying scattered being picked up by birds. Improved planting method may lead to increased production of maize which will result in attaining self-sufficiency in food and feed . PlantinNitrogen (N) is a vital plant nutrient and a major determining factor required for maize production . It is v+ diffusion in the soil towards the roots are both restricted, thus limiting K acquisition. The resulting lower K concentrations can further depress the plant resistance to drought stress, as well as K absorption. Maintaining adequate plant K is, therefore, critical for plant drought resistance. A close relationship between K nutritional status and plant drought resistance has been demonstrated [Potassium is an essential nutrient and is also the most abundant cation in plants. It plays essential roles in enzyme activation, protein synthesis, photosynthesis, osmoregulation, stomatal movement, energy transfer, phloem transport, cation-anion balance, and stress resistance. A major limitation for plant growth and crop production under rainfed condition is soil water availability. Plants that are continuously exposed to drought stress can form reactive oxygen species (ROS), which leads to leaf damage and, ultimately, decreases crop yield. During drought stress, root growth and the rates of Knstrated . KeepingKerawa) Agriculture Research Station, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Budgam. The area lies between 34° 0.8′ N latitude and 74° 83′ E longitude at an altitude of 1587 meters above the mean sea level. The mean maximum temperature ranged from 24.3 to 31.5°C and the minimum from 9.7 to 17.60°C during the first growing season and from 21.2 to 32.2°C and from 8.2 to 19.8°C during the second growing season. The total rainfall received during the entire growing seasons of 2010-11 and 2011-12 amounted to 383.70 mm and 426.10 mm, respectively. The experiment was laid out in a randomized block design with combination of 2 sowing methods  and 3 fertility levels  with three replications. Prior to sowing, the field site was ploughed three times approximately 30 cm deep using a cultivator to destroy offtypes. Fine seed beds were prepared prior to sowing. The maize variety “C-6” was sown at a spacing of 75 cm × 20 cm between rows and plants. The trial was irrigated when required. Full dose of phosphorus and potassium and 1/3rd of nitrogen were band placed as per the treatment just before sowing of seed. The remaining nitrogen was top dressed in two equal splits at knee high and tasselling stages. Fertilizer nitrogen, phosphorus, and potassium were applied through urea, diammonium phosphate, and muriate of potash, respectively.The investigation was conducted during kharif of 2011 and 2012 at Dryland (−1), were recorded from penultimate rows of each plot. The leaf area of each leaf was calculated by multiplying the length and maximum width. The value thus obtained was multiplied by a constant 0.73309 to get actual leaf area and then leaf area index (LAI) was calculated by dividing the leaf area per plant by ground surface provided to each plant (1200 sq. cm). Five randomly selected plants in each plot were tagged for various periodic observations. The same procedure was followed for both the years. For recording of data on yield attributes, namely, cob length (cm), cobs plant−1, grains cob−1, cob diameter (cm), number of rows cob−1, and 100-grain weight (g), the number of cobs of five randomly selected plants from each plot was used. The grain yield of each net plot was thoroughly cleaned and sun-dried. The yield from each plot was recorded separately as kg plot−1 and then converted in q ha−1. After removal of the cobs from stalks in each net plot, the stalks were weighed to determine the stover yield in q ha−1.Growth parameters, namely, plant height (cm), leaf area index, days to reach different phenological stages , and dry matter production  compared to F1 (60 : 40 : 20); however, the fertility level F3 (90 : 60 : 40) did not differ from F2 (75 : 50 : 30). This could be attributed to a mere fact that higher rates of nitrogen may have caused rapid cell division and elongation. Sarwargaonkar et al. [1 (60 : 40 : 20) to F2 (75 : 50 : 30) significantly improved leaf area index at different crop growth stages and beyond F2 (75 : 50 : 30) level, the difference was nonsignificant. Maximum leaf area was recorded at tasseling stage. F2 (75 : 50 : 30) level might have provided sufficient nitrogen to the crop for rapid cell division and cell elongation thereby resulting in increased leaf area. Shivay and Singh [3 (90 : 60 : 40) significantly increased number of days for crop to reach different phenological stages. Increased dose of nitrogen might have lengthened the vegetative phase of the crop, thereby delaying the reproductive period of the crop. The study revealed a gradual increase in dry matter production of crop from knee high to maturity stage irrespective of fertility levels and the magnitude of increase was highly pronounced from knee high to tasseling stage. This could be attributed to vigorous growth of crop in terms of gain in plant height and higher number of functional leaves per plant. Moreover, higher leaf area index of the crop at tasseling indicates higher crop growth rate from knee high to tasseling stage thereby resulting in higher dry matter production which could have resulted from higher plant height and higher number of functional leaves. Fertility level F2 (75 : 50 : 30) significantly improved dry matter production over F1 (60 : 40 : 20) but was nonsignificant with F3 (90 : 60 : 40) level. Higher leaves and higher LAI paved the way for more production of photosynthetic dry matter. Similar results have also been reported by Sarwargaonkar et al. [Data  indicater et al.  reportedr et al. .2 (75 : 50 : 30) level beyond which difference was nonsignificant (2 (75 : 50 : 30) level might be due to sufficient supply of nitrogen to the crop because nitrogen being an essential constituent of plant tissue is involved in cell division and cell elongation. Moreover, higher leaf area index values noticed at F2 (75 : 50 : 30) level mean the production of more photosynthates leading to increase in grain number and weight of grains. Rasheed et al. [The investigation revealed that yield contributing characters, namely, cob length and diameter, number of cobs per plant, grain rows, number of grains per cob, and 100-grain weight, increased significantly up to Fnificant . Higher d et al.  have alsd et al. .3 (90 : 60 : 40) being at par with F2 (75 : 50 : 30) recorded the increased yield over F1 (60 : 40 : 20) with superiority of 5.4 and 5.7 per cent over F1 during 2011 and 2012, respectively. The yield components, namely, cobs per plant, grains per cob, and grain weight, increased significantly up to F2 (75 : 50 : 30) level; thereby the combined effect of these components resulted in yield increase. A similar effect of fertilizer levels on maize yield and its components was reported by Bakht et al. [3 (90 : 60 : 40) and F2 (75 : 50 : 30) at par with one another recorded significant increase in stover yield over F1 (60 : 40 : 20) during both the years of the study. The higher uptake of nutrients by the crop produced higher LAI meaning more production of photosynthates leading to higher dry matter production in terms of grain and stover yield. Abdullah [The study revealed that treatments influenced the seed yield significantly during both years of experimentation . Fertilit et al.  and MaqsAbdullah  and GhafAbdullah  also rep2 treatment (2 (ridges) provides loose fertile soil with more aeration and moisture availability; therefore, improved soil condition along with better uptake of nutrients might have provided better environment to crop resulting in improved plant height. Khan et al. [The study revealed significant enhancement in plant height with Sreatment . In factn et al.  have alsn et al.  have alsn et al.  and Khann et al. .The study revealed that ridge sowing significantly improved cob length and diameter, grain rows, number of grains per cob, and 100-grain weight . Higher Grain yield is the product of cobs per unit area, grain per cob, and grain weight. During the study it was found that cobs per plant, grains per cob, and 100-grain weight were maximum in ridge system thereby because their cumulative effect increased the grain yield . The pro−1, 50 kg P ha−1, and 30 kg K ha−1 under ridge method of sowing was found to be an appropriate treatment for growing rainfed maize and can be recommended for farmers of Kashmir Valley.Various aspects of the present investigation and observation generated showed that all growth and yield and yield attributing traits were discernibly influenced by manipulation in sowing methods and NPK level. Results clearly suggested that, for temperate environment of Kashmir Valley, the application of 75 kg N ha


By 28 June 2015, there were a total of 11,234 deaths from the Ebola virus disease (EVD) in five West African countries . The objective of this study was to estimate the future productivity losses associated with EVD deaths in these West African countries, in order to encourage increased investments in national health systems.A cost-of-illness method was employed to calculate future non-health (NH) gross domestic product (GDP) (NHGDP) losses associated with EVD deaths. The future non-health GDP loss (NHGDPLoss) was discounted at 3 %. Separate analyses were done for three different age groups  for the five countries  affected by EVD. We also conducted a one-way sensitivity analysis at 5 and 10 % discount rates to gauge their impacts on expected NHGDPLoss.The discounted value of future NHGDPLoss due to the 11,234 deaths associated with EVD was estimated to be Int$  155,663,244. About 27.86 % of the loss would be borne by Guinea, 34.84 % by Liberia, 0.10 % by Mali, 0.24 % by Nigeria and 36.96 % by Sierra Leone. About 27.27 % of the loss is attributed to those aged under 14 years, 66.27 % to those aged 15–44 years and 6.46 % to those aged over 45 years. The average NHGDPLoss per EVD death was estimated to be Int$ 17,473 for Guinea, Int$ 11,283 for Liberia, Int$ 25,126 for Mali, Int$ 47,364 for Nigeria and Int$ 14,633 for Sierra Leone.In spite of alluded limitations, the estimates of human and economic losses reported in this paper, in addition to those projected by the World Bank, show that EVD imposes a significant economic burden on the affected West African countries. That heavy burden, coupled with human rights and global security concerns, underscores the urgent need for increased domestic and external investments to enable Guinea, Liberia and Sierra Leone (and other vulnerable African countries) to develop resilient health systems, including core capacities to detect, assess, notify, verify and report events, and to respond to public health risks and emergencies.The online version of this article (doi:10.1186/s40249-015-0079-4) contains supplementary material, which is available to authorized users. Please see Additional file The estimated population of the six West African countries  affected by the Ebola virus disease (EVD) was 225,181,000, representing 24.3 % of the entire African Region population. The annual population growth rate varied from 2.4 % in Guinea to 3.2 % in Liberia between 2003 to 2013. Between 36 and 49 % of the population lives in urban areas – a sizeable proportion resides in overcrowded and unsanitary informal settlements. The literacy rate among adults aged 15 years and above varied from 33 % in Mali to 61 % in Liberia. The gross national income per capita (GDPPC) varied between Int$  790 in Liberia to Int$ 5360 in Nigeria. The percentage of population living below Int$ 1 per day varied from 34.1 % in Senegal to 83.8 % in Liberia [Physicians, nursing and midwifery personnel, dentistry personnel, pharmaceutical personnel and psychiatrists are the key cadres of the health system. Except for in Nigeria – where the densities of physicians and nursing and midwifery personnel are higher than the African Region averages – the workforce densities were much lower than both the regional and global averages in the other five West African countries even before the EVD outbreak. The density of hospitals is also lower in EVD-affected countries compared to the regional and global averages. In addition, only Nigeria was spending 18 % of the total government budget on the health sector. This implies that even before the EVD outbreak occurred the other five countries did not meet the Abuja target of allocating at least 15 % of the national budget to the health sector. The per capita total expenditure on health (PCTEH), which was between Int$ 56 and Int$ 184 in EVD-affected countries, was lower than the African Region Int$ 208) and global (Int$ 1173) averages [08 and glDue to relatively weak national health systems, the EVD outbreak that was first reported in March 2014 in Guinea rapidly spread to other West African countries. As of 28 June 2015, a total of 27,543 cases of EVD were reported, of which 13.539 % were in Guinea, 38.725 % in Liberia, 0.029 % in Mali, 0.0726 % in Nigeria, 0.0034 % in Senegal and 47.631 % in Sierra Leone. The cumulative number of EVD-related deaths was 11,234, with 22.094 % of these in Guinea, 42.781 % in Liberia, 0.054 % in Mali, 0.071 % in Nigeria and 35 % in Sierra Leone [According to the World Bank, apart from the loss of human lives and suffering, the EVD epidemic has also had a significant impact on the economies of the affected countries in terms of ‘…forgone output; higher fiscal deficits; rising prices; lower real household incomes and greater poverty’ . The WorThis paper attempts to answer the question: What is the impact of deaths associated with EVD on future non-health GDP (NHGDP)  in the West African Region? The specific objective was to estimate future productivity losses of deaths associated with EVD in the West African Region in an effort to complement the World Bank projections and advocate for increased investments in health systems (including disease surveillance).Fear, panic, morbidity and deaths associated with EVD had a negative impact on the economies of affected countries, especially Guinea, Liberia and Sierra Leone. This included the effects on real economic activities such as production, consumption, saving, investment, imports and exports. Changes in real economic activities, for example, changes in business cycles or in economic growth due to EVD would consequently affect future GDP. The GDP is the sum of households’ consumption expenditures (C), investment (I), government expenditures (G) and net exports (X), i.e., exports minus imports .The category of ‘C’ comprises expenditures on consumable goods and services, such as health, education, food, clothing, transport, books, stationery, cooking utensils, entertainment, toiletries, newspapers, bicycles and motorcycles, private vehicles, etc. ‘C’ depends on real disposable income , wealth, the average price level, expectations, habits, demographic factors, etc.The category ‘I’ denotes the purchase of capital goods or real assets on which a return is expected from the sales of production, e.g., buildings, public vehicles, tractors and machinery. ‘I’ depends on interest rates, expectations, business confidence and regulations. Usually money for investment comes from saved disposable income.The category ‘G’ denotes purchase of goods and services by the government, e.g., roads, security forces (police and armed forces), medicines and supplies for public health facilities, stationary and salaries of civil servants. ‘G’ depends on total tax revenues (which depend on incomes) and borrowing.The category ‘X’ refers to net exports, i.e., exports minus imports. Exports denote spending by foreigners on goods  and services  produced in EVD affected countries. The factors that determine exports include prices of locally produced goods relative to prices of imported goods (terms of trade), the exchange rate between local and foreign currencies, total income in foreign countries, trade policy in foreign countries  and trade sanctions or boycotts or bans. For example, some foreign countries banned exports of goods and services from EVD affected countries.Imports refer to purchase of foreign consumer  and capital goods  by citizens of EVD affected countries households, business enterprises, governments and parastatal institutions. Import expenditures are dependent on total income in EVD affected countries, prices of imported goods relative to the prices of locally produced goods, exchange rate, trade policy  and trade sanctions or boycotts or bans. For instance, some countries banned their airlines from flying to EVD affected countries and recalled non-medical expatriates hitherto working in those countries.In EVD-affected countries, large numbers of people living off the agricultural sector fell seriously ill, some died and others fled their farms due to fear of contracting EVD. This in turn lead to a reduction in output; some mining, manufacturing and service industries closed down, and the volume of exports also diminished with the ban of commodities from EVD-affected countries. The decrease in economic activity and the ensuing increase in unemployment led to a decrease in disposable income and, hence, consumption, tax revenues, savings and investments .This study employs a cost-of-illness method to estimate the impact of deaths associated with EVD on non-health components of future GDP , 7. The The non-health GDP loss (NHGDPLoss) for each of the five EVD-affected countries was estimated separately for three different age groups: 0–14 years, 15–44 years and 45 years and above. The WHO EVD situation document reports numbers of cases and deaths classified in these same age categories .th country (NHGDPLoss0 − 14) is the product of the total number of discounted years of life lost (DYLL) above the minimum employment age lost, the per capita non-health GDP (NHGDPPC) in purchasing power parity (PPP) (NHGDPPCInt$) and the total number of deaths associated with EVD (TEVDD) in that age group (TEVDD0 − 14). The number of years of life lost (YLL) for this age group was calculated by subtracting the average age at death (AAD) (AAD0 − 14) from each country’s life expectancy at birth (LE). AAD0 − 14 is equal to 0 plus 14 years divided by 2, (0 + 14/2) = 7. A simple average was used, as the WHO EVD situation report, dated 1 July 2015, didn’t show the distribution of deaths by age. Because the legal minimum age of employment is 15 years, as according to article 2 of the International Labour Organization convention [AADLE45 −   and years remaining to attain the minimum age of employment .The NHGDPLoss due to deaths associated with EVD for the 0–14 years age group in jnvention , the numThe formula for the 0–14 years age group is expressed as follows:th country (NHGDPLoss15 − 44) is the product of the total number of DYLL, NHGDPPC in PPP (NHGDPPCInt$) and the TEVDD in that age group (TEVDD15 − 44). The number of YLL for this age group was calculated by subtracting the AAD (AAD15 − 44) from each country’s average LE. AAD15 − 44 is equal to 15 plus 44 years divided by 2, (15 + 44/2). The formula for the 15–45 years age group is as follows:The NHGDPLoss due to deaths associated with EVD for the 15–44 years age group in jth country (NHGDPLossLE45 − ) is the product of the total number of DYLL, NHGDPPC in PPP (NHGDPPCInt$) and TEVDD in that age group (TEVDDLE45 − ). The number of YLL for this age group was calculated by subtracting the AAD (AADLE45 − ) from each country’s average LE. AADLE45 −  is equal to 45 plus LE divided by 2, e.g., as LE of Guinea was 58 years, AADLE45 −  = (45 + 58/2) = 51.5. The formula for the 45 years and above age group is as follows:Similarly, the NHGDPLoss due to deaths associated with EVD for those aged between 45 years and the age of LE in jr)t is the discount factor, r is the rate of discount of future losses, t is the first year of life lost and n is the final year of the total number of YLL per death associated with EVD. Thus, NHGDPPCInt$ is obtained by subtracting the per capita total health expenditure (PCTHE) from the per capita GDP (Int$ GDPPC) for each EVD-affected country. We used 2013 as the year on the basis of which losses potentially occurring in future years were discounted. The size of the discount factor decreases as one moves from the base year into future years, and, as a result losses in successive years are given a lower value relative to the same losses in the first year. This is due to existence of time preference – meaning individually and as a society we prefer to have dollars or resources now as opposed to later because we can benefit from them in the interim. Therefore, as Drummond et al. further explains, future dollar cost and benefit streams are reduced or ‘discounted’ to reflect the fact that dollars spent or saved in the future should not weigh as heavily in programme decisions as dollars spent or saved today [r, but also on the number of years, t, over which the discounting has been applied. When discounting is applied so that all GDP losses are re-valued relative to year 0 , the re-valued values are called present values [In all three equations, 1/ = (a) × (c) = 392 TEVDD in the 0–14 years age group as of 28 June 2015 (TEVDD15 − 44) = (a) × (d) = 1438 TEVDD in the 15–44 years age group as of 28 June 2015 (TEVDD45 − 58) = (a) × (e) = 652 TEVDD in the 45–58 years age group as of 28 June 2015 (AAD0 − 14) = (0 + 14/2) = 7 yearsAAD in the 0–14 years age group (Years remaining to attain minimum employment age = 14 – 7 = 7 yearsAAD0 − 14 +7) = 58 – (7 + 7) = 44 years Undiscounted PYLL in the 0–14 years age group = LE – (Discounted PYLL in the 0–14 years age group = 24.25427392 yearsAAD15 − 44) = (15 + 44/2) = 29.5 years AAD in the 15–44 years age group (AAD15 − 44) = 58 – 29.5 = 28.5 years Undiscounted PYLL in the 15–44 years age group = LE – ( Discounted PYLL in the 15–44 years age group = 19.18845459AAD45 − 58) = (45 + 58/2) = 51.5 years AAD in the 45–58 years age group (AAD15 − 44) = 58 – 51.5 = 6.5 years Undiscounted PYLL in the 45–58 years age group = LE – (Discounted PYLL in the 45–58 years age group = 6.230282955Int$ GDPPC = Int$ 1129.352PCTHE = Int$ 75.836 NHGDPPC = GDPPC (Int$ 1129.352) − PCTHE (Int$ 75.836) = Int$ 1053.516r = 3 %NHGDPLoss0 − 14) = (L)x(u)x(f) = 24.25427392 × 1053.516 × 392 = Int$ 10,016,488.13 (NHGDPLoss15 − 44) = (o)x(u)x(g) = 19.18845459 × 1053.516 × 1438 = Int$ 29,069,665 (NHGDPLoss45 − 58) = (r)x(u)x(h) = 6.230282955 × 1053.516 × 652 = Int$ 4,279,534.21 + (x) + (y) = Int$ 10,016,488 + Int$ 29,069,665 + Int$ 4,279,534 = Int$ 43,365,687The example below showing the calculation of loss associated with deaths from EVD in non-health GDP uses actual information on Guinea:The above formulas were calculated using Excel to avoid errors. The NHGDPLoss for the other four West African countries were estimated the same way.The data on LE, total child deaths, the GDPPC in PPP and PCTHE for each of the six EVD-affected West African countries were obtained from the World Health Statistics 2015 [This study was entirely based on analysis of published secondary data available in the public domain. Therefore, given that no human subjects were involved, the study did not require ethical clearance from the Ethics Review Committee of the WHO Regional Office for Africa.The 11,234 EVD deaths that occurred in the African Region as of 28 June 2015 could potentially decrease future total NHGDP of the affected countries by Int$ 155,663,244 , the NHGDPLoss per death associated with EVD was relatively high at Int$ 47,364. Low-income countries such as Guinea, Liberia and Sierra Leone with relatively high TEVDD of 2482, 4806 and 3932 have comparatively low productivity losses per death associated with EVD of Int$ 17,473, Int$ 11,283 and Int$ 14,633, respectively.The average NHGDPLoss per person for the five countries is shown in Table We applied a discount rate of 3 % to convert future loses into their present values, as this rate had been previously used in various arenas, including the WHO health systems’ performance assessment , global It is expected that the five West African countries will sustain a loss of about 0.03 % of their non-health GDP due to future YYL, as a result of 11,234 EVD-related deaths. Majority (66.27 %) of the total NHGDPLoss was attributed to deaths that occurred in those aged 15–44 years, i.e., the most productive bracket. And about a quarter of the NHGDPLoss was attributed to deaths that occurred in those aged 14 years or below, which may imply intergenerational economic effects of the EVD public health emergency.The reader will recall that the World Bank projected that the combined GDP loss for Guinea, Liberia and Sierra Leone, the three countries most severely affected by EVD, would be US$ 359 million in the short term 2014), US$ 129 million in a low Ebola prevalence scenario in the medium term (2015), and US$ 815 million in a high Ebola prevalence scenario in the medium term (2015) [, US$ 129In order to prevent the recurrence of the human and economic losses reported in this paper, in addition to those projected by the World Bank, there is an urgent need for increased investments in national health systems –18 – as In addition, the EVD outbreak in West Africa underscores the urgency of mobilizing domestic (public and private sector) and external resources to support full implementation of the International Health Regulations 2005) (IHR), which obliges for all States Parties to establish at local, intermediate and national public health response levels core capacities to detect, assess, notify, verify and report events, and to respond to public health risks and emergencies . The WHO05 (IHR),The three most affected countries  have developed health sector recovery plans, which, if fully funded and efficiently implemented, are expected to result in resilient health systems characterised by safe and healthy work settings; increased primary healthcare facility coverage in all areas; increased skilled workforce; increased community trust, ownership and access to essential health services; strengthened epidemic preparedness, surveillance and response compliant with international health regulations; improved health system governance; efficient intersectoral action on social determinants of health; and effective cross-border collaboration –23. FollThe present study had five main limitations. First, it does not include costs of alternative interventions that could prevent EVD-related morbidity and mortality and, thus, it cannot inform public health priority settings , 26. TheSecond, our study did not include direct healthcare costs such as those related to vaccines, drugs, tests, supplies, hospital, building and equipping EVD treatment centres, personnel involved in contact tracing and caring for patients, diagnostic and waste disposal equipment and physical facilities; direct non-healthcare costs of treatment such as transport; patients’ time costs; costs of volunteers’ time, or cost of family or friends accompanying patients to healthcare centres; loss in productivity due to morbidity before premature death; and intangible costs such as pain and grief due to premature loss of loved ones . Thus, aThird, GDP calculations omit housework, including rearing children, preparing meals, caring for sick household members, and nursing and/or giving other assistance to the elderly. According to Becker, this undervalues the contribution of women, who are primarily responsible for household duties . In addiFourth, because EVD  put a maLastly, the present study excludes the negative impact of EVD-related fear, panic and morbidity on exports, agriculture, industry  and education due to closure of schools.Develop resilient local and national health systems ;Develop core IHR capacities ;Strengthen the interface between health and all other relevant sectors, including animal health, education, food, housing, transport, travel, tourism, water, sanitation, etc. ;Strengthen the governance of national system of research for health to ensure production pertinent evidence and its use in the process of developing various systemic capacities ; andStrengthen political leadership and governance capacities at local, intermediate and national levels , 33.In spite of alluded limitations, the estimates of human and economic losses reported in this paper, in addition to those projected by the World Bank, show that EVD imposes a significant economic burden on the affected West African countries. That heavy burden, coupled with human rights and global security concerns, underscores the urgent need for increased domestic and external investments to, in solidarity, support Guinea, Liberia and Sierra Leone (and other vulnerable African countries) to:Economic feasibility of various systems of ensuring universal health coverage.Feasibility of community/village multi-sectoral development committees ; community/village health committees; and health facility management committees (with community representation).Current core IHR capacities, bottlenecks and economically feasible ways of developing and sustaining IHR capacities.Functionality of community and district health systems: management structures, managerial processes, policy to guide health sector reforms at district level, collaboration with other actors ; availability and use of guidelines, standards and norms at health facilities; supervision and monitoring; medicines management; referral mechanisms; health management information system; priority health activities; community involvement; health and health-related resources funding and financial management; human resources; infrastructure, equipment, supplies and logistics.Performance of national system of research for health and actions for improvement.Feasible approaches for changing negative health-related community behaviours, which makes them vulnerable to infectious diseases such as EVD.In addition, there is urgent need for further research to determine:


The transitive inference (TI) paradigm has been widely used to examine the role of the hippocampus in generalization. Here we consider a surprising feature of experimental findings in this task: the relatively poor transitivity performance and levels of hierarchy knowledge achieved by adult human subjects. We focused on the influence of the task instructions on participants’ subsequent performance—a single-word framing manipulation which either specified the relation between items as transitive  or left it ambiguous . We show a marked but highly specific effect of manipulating prior knowledge through instruction: transitivity performance and levels of relational hierarchy knowledge were enhanced, but premise performance unchanged. Further, we show that hierarchy recall accuracy, but not conventional awareness scores, was a significant predictor of inferential performance across the entire group of participants. The current study has four main implications: first, our findings establish the importance of the task instructions, and prior knowledge, in the TI paradigm—suggesting that they influence the size of the overall hypothesis space . Second, the dissociable effects of the instructional frame on premise and inference performance provide evidence for the operation of distinct underlying mechanisms . Third, our findings suggest that a detailed measurement of hierarchy recall accuracy may be a more sensitive index of relational hierarchy knowledge, than conventional awareness score—and should be used in future studies investigating links between awareness and inferential performance. Finally, our study motivates an experimental setting that ensures robust hierarchy learning across participants—therefore facilitating study of the neural mechanisms underlying the learning and representation of linear hierarchies. The transitive inference (TI) paradigm has been extensively used across species to investigate the cognitive mechanisms supporting generalization—a term we use here to describe the flexible expression of prior learning in novel situations , through the exploitation of structure present within a set of related episodes . In contrast, previous studies either assessed the effect of manipulating the overall instructions received by participants .Thirty healthy individuals participated in this experiment . All participants gave informed written consent to participation in accordance with the local research ethics committee. Each group  comprised 15 participants. There was no significant differences between subject groups in terms of age, years of higher education, or performance on an abbreviated form  of the Raven's progressive matrices . The galaxy hierarchy consisted of eight items . The allocation of individual pictures to position in the hierarchy was randomized across the group of participants. Stimuli were presented using Cogent Graphics toolbox (http://www.vislab.ucl.ac.uk/cogent_graphics.php) operating in a MATLAB 7 environment.Pictures of galaxies were obtained from various sites on the Internet . The left-right position of an item on the screen was randomized across trials. Participants had 5 seconds in which to choose, via button press , the item which they thought was “correct” (NO-FRAME condition) or “older” (OLD-FRAME condition). After participant's response, a feedback screen appeared: this consisted of a green square border that indicated the participant's choice together with either “+20 points” or “−20 points”, for a correct or incorrect response, respectively. A fixation cross preceded the onset of the next trial.Test trials involved the presentation of the seven premise pairs as well as nine inference pairs . As in training trials, participants had 5 seconds in which to choose, via button press , the galaxy which they thought was “correct” (NO-FRAME condition) or was “older” (OLD-FRAME condition). Importantly, feedback was not presented during test trials, although participants were instructed that their choices would still count toward their final payout. Instead, after participant's response, a screen appeared that required participants to rate  their confidence in their decision: participants were carefully instructed to enter a “1” response if they were guessing entirely, a “2” response if they “had some idea but were not sure” about their choice, and to reserve a “3” response until they were “more than 90% certain” that their choice was the correct one. Participants were told that although their confidence responses would not count toward their final payout, they should still answer as accurately as possible. Note that although subjective confidence responses were recorded in this experiment, here we restrict our analyses to the binary choice data.The remuneration received by participants was determined directly from the number of correct responses during training and test trials, in addition to a basic minimum for participation in the experiment.Participants completed two experimental sessions (with a 1-minute break in between), comprising six blocks each. Each block consisted of a miniblock of training trials followed by a miniblock of test trials. Each training trial miniblock consisted of 21 trials in total made up from three repetitions of each of the seven premise pair types  presented in pseudorandom order. Each test trial miniblock consisted of 16 trials in total made up from one repetition of each of the seven premise pairs , and one repetition of each of the nine inference pairs , presented in pseudorandom order. The start of each miniblock was preceded with the relevant instruction . Following the end of each miniblock, a screen showing the percentage of correct responses achieved was displayed.Following the completion of the experiment, participants were carefully debriefed so as to evaluate the presence and nature of explicit knowledge concerning the task structure. A debriefing questionnaire  that was based closely on that employed by Moses et al.  also se was admiParticipants' performance on the hierarchy recall test was scored in two ways: (1) on a two-point scale, as in Moses et al., Behavioral analyses were conducted using SPSS version 19 using standard procedures. The analyses presented here focus on the binary choice data obtained during training and test trials: the test trial confidence data are not considered for the present purposes. Performance accuracy and reaction time were analyzed for both training blocks and test blocks, using repeated measures mixed factor analyses of variance (ANOVA). Mauchly's test was used to evaluate whether the assumption of sphericity had been violated, and degrees of freedom were corrected using Greenhouse-Geisser estimates of sphericity when appropriate.F = 56.0, P < 0.001) was significant, with a significant linear effect  = 118.7, P < 0.001). Main effect of group and interaction of block and group were not significant (P > 0.2). For RT: main effect of block  = 7.18, P < 0.001) was significant, with a significant linear effect =18.2, P < 0.001). Main effect of group and interaction of block and group were not significant (P > 0.2).We first considered the performance (% correct and reaction time (RT)) of both groups of participants over the 12 blocks of training trials. During the last block of training trials, the NO-FRAME group averaged 90.2% correct responses (SD 16.3), and the OLD-FRAME group averaged 92.4% correct responses (SD 9.0)see A. Data wF = 20.7, P < 0.001) was significant, with a significant quadratic effect  = 116.3, P < 0.001). Main effect of group and interaction of premise pair type and group were not significant (P > 0.2). For RT: we observed a main effect of premise pair type  = 56.8, P < 0.001). No effect of group, or group by premise pair interaction was observed (P > 0.2).We next considered the performance of participants on training trials, as a function of premise pair see B. Data wF = 61.9, P < 0.001) reflecting superior performance on trials involving outer pairs. Indeed, performance on outer pairs was significantly better than on inner pairs when the outermost premise pairs were excluded from this analysis  = 64.5, P < 0.001). For RT: a main effect of pair type was also found  = 81.1, P < 0.001) reflecting shorter reaction times on trials involving outer pairs.As a follow-up test, we explored the main effect of premise pair type further in a mixed-design ANOVA where premise pairs were grouped into outer pairs  and inner pairs : a main effect of pair type was found  = 9.4 P < 0.01)) and a paired-sample t-test (GH > AB: t(29) = 3.1, two-tailed test P < 0.01). Reaction times were also significantly faster during GH trials, as compared to AB trials: t(29) = 2.1, two-tailed test P < 0.05).We further observed that performance (% correct) was superior during GH trials, as compared to AB trials as evidenced by the results of a mixed-design ANOVA  was similar in both subject groups, and consistent with the operation of associative learning mechanisms . The average performance of participants in the NO-FRAME group on premise pairs and inference pairs across all test trial blocks was 81.1% (SD 10.5) and 62.6% (SD 14.4), respectively. The average performance of participants in the OLD-FRAME group on premise pairs and inference pairs was 82.5% (SD 5.9) and 76.7% (SD 10.1), respectively see A,B. DuriF = 29.2, P < 0.001), block  = 29.8, P < 0.001), and group  = 6.0, P < 0.05) were significant. There were significant interactions between trial type and group  = 7.9, P < 0.01), and trial type and block  = 2.3, P < 0.05). A borderline significant result was observed for the interaction between block and group  = 2.2, P = 0.06). For RT: main effects of trial type  = 38.3, P < 0.001), block  = 17.8, P < 0.001), and block by group interaction  = 2.6, P < 0.05) were significant. The main effect of group was not significant (P > 0.1). A follow-up paired sample t-test showed that premise pair RT was significantly faster  than inference pair RT  = 5.7, P < 0.001).Data were analyzed using a mixed-design ANOVA with two within-subject factors of trial type  and block, and one between-subject factor . For % correct responses: main effects of trial type  = 9.6, P < 0.01), as well as a main effect of block  = 14.5, P < 0.001). Of note there was also a significant block × group interaction  = 2.6, P < 0.05). This was shown in a follow-up ANOVA to be due to significantly superior inference pair performance in the OLD-FRAME group  in the second half of the experiment  = 14.4, P < 0.001), but not in the first half (P > 0.2). In contrast, an ANOVA focusing on premise pair performance  revealed no significant main effect of group or block by group interaction (both Ps > 0.2), although a significant main effect of block was present  = 24.1, P < 0.001).A further ANOVA confirmed that the significant main effect of group (% correct responses), and interaction between group and trial type, was driven by superior performance of the OLD-FRAME group compared to the NO-FRAME group on inference pairs. Specifically, an ANOVA focusing on inference pair performance  revealed a significant main effect of group  =15.9, P < 0.001) as well as a significant main effect of group  = 7.6, P < 0.01). Although there was a borderline significant distance × group interaction: F = 3.1, P = 0.06), we found a main effect of distance when each subject group was analyzed separately: NO-FRAME  = 11.6, P < 0.001) and OLD-FRAME  = 4.7, P < 0.05)), with both effects linear in nature (both Ps <0.01). No significant effects were seen in relation to RT, either in terms of a main effect of distance or a distance by group interaction (both Ps >0.1).We next examined how performance on inference pairs varied as a function of the distance between the position of items on the hierarchy  = 14.9, P < 0.001 and F = 7.8, P = 0.01, respectively. In contrast, no difference was found between performance of the two groups in distance 4 inference pairs: P > 0.2  of the OLD-FRAME group  in distance 2  and distance 3  inference pairs:  0.2 see D.F = 8.3, P < 0.01.The average of the NO-FRAME group on the “awareness” post-experimental questionnaire test (maximum score achievable = 12 points) was 6.9 (SD 2.1)—that of the OLD-FRAME group was 9.5 (SD 2.7). These scores were significantly different: one-way ANOVA P < 0.05). The average number of errors made on the hierarchy recall test  for the NO-FRAME and OLD-FRAME groups were 7.6 (SD 8.1) and 2.7 (SD 5.6), respectively  = 3.8, P = 0.06.Considering participants performance on the hierarchy recall test specifically: 5 out of 15 participants in the NO-FRAME were able to perfectly recall the hierarchy, as compared to 11 out of 15 participants in the OLD-FRAME group: these values were significantly different from each other —and also with awareness score . Considering each group separately, in the NO-FRAME group, there was a significant correlation between inferential performance  and the accuracy of hierarchy recall , but not awareness score . Note that the correlation of hierarchy recall accuracy with inferential performance was also significant when only the last block of test trials was considered —as before, no correlation with awareness score was observed (P > 0.1). In the OLD-FRAME group, both debriefing measures showed a significant correlation with inferential performance .We next asked whether participants' score on the awareness questionnaire, and their ability to accurately recall the hierarchy order, correlated with their performance on inference pairs during test trials. When both groups were considered together, there was a significant correlation between performance on inference test pairs  and number of hierarchy recall errors  = 11.1, P < 0.001) that was related to a significant effect of hierarchy recall errors . A similar result was also observed when inferential performance was restricted to the last test trial block  = 23.6, P < 0.001): with a significant effect of hierarchy recall errors  but not awareness score . Taken together, these results suggest that high levels of relational hierarchy knowledge, but not high levels of awareness, were predictive of successful inferential performance.Finally, we performed a multiple regression analysis where the relationship between inferential performance  and the two debriefing measures  was assessed, across the whole group of 30 participants. We found a significant overall effect  to one that left it ambiguous (“correct”). We report three key findings: first, our instructional manipulation had a highly specific effect on inferential performance, whilst leaving premise pair performance entirely unaffected. Secondly, we show that the OLD-FRAME ensured the development of robust relational hierarchy knowledge across the subject group—to a degree that was significantly greater than that achieved by the NO-FRAME group. Finally, we show that hierarchy recall accuracy, but not awareness scores, was a significant predictor of inferential performance across the entire group of participants—suggesting that this measure may be a more sensitive index of relational hierarchy knowledge. Taken together, our findings provide insights into the relatively poor performance of participants in contemporary TI experiments, motivate a version of the TI paradigm that ensures robust hierarchy learning, and highlight the critical importance of distinguishing between measures of hierarchy knowledge, and more general measures of awareness.It is important to consider our experimental paradigm and findings in relation to two previous studies which share similar aims. We first summarize their principal findings: in the study by Greene et al. , particiOur findings, therefore, converge with previous work in demonstrating that the relatively poor performance of healthy young participants on contemporary versions of the TI task can be markedly improved through manipulating the amount of prior knowledge—either through instruction  that facilitates performance in the TI task—point toward a potential mechanism that may mediate the effects of instruction on transitivity performance. Specifically, instructing participants to choose the “older” item likely increased the prior over a linear hierarchical structure—a structural form intimately related to the concept of transitivity—in effect narrowing the hypothesis space through the activation of an “overhypothesis” . Whilst substantial work has focused on the critical role of the hippocampus . Interestingly, participants in the OLD-Frame group performed significantly better than the NO-Frame group at transitive inferences involving a smaller symbolic distance , but there was no significant difference between the groups at a larger symbolic distance . Whilst mechanisms based on both associative learning and hierarchy knowledge are known to produce symbolic distance effects of this nature Breslow, , our datOur study also has implications for a contentious debate surrounding the relationship between the ability to make transitive inferences during inference trials and measures of “awareness” (Greene et al., Our findings, therefore, suggest the need for caution when interpreting previous reports that successful transitivity performance is possible in the absence of explicit knowledge of the underlying hierarchy (e.g., Greene et al., Linear hierarchies can be considered a prototypical form of knowledge structure that have a wide-ranging influence across cognitive domains (Kemp and Tenenbaum, Taken together, our study has several implications: first, we establish the importance of task instructions in determining performance in the TI paradigm—and show that prior knowledge that item relations are transitive facilitates inference performance and the development of hierarchy knowledge, likely by constraining the space of possible task solutions. Second, our findings demonstrate that hierarchy recall accuracy, but not awareness scores, was a significant predictor of inferential performance across the entire group of participants—suggesting that this measure may be the more sensitive index of relational hierarchy knowledge. Finally, our instructional manipulation—combined with a specific measure of hierarchy recall accuracy—motivates an experimental setting which lends itself to further characterization of the cognitive and neural mechanisms underlying hierarchy knowledge.


Health care systems in Finland, Norway and Sweden share many similarities, e.g. full-coverage and tax-financed, with predominately public sector hospitals. Despite similarities, there are differences in the working situations for RNs within these Nordic countries. The aim of this study was to analyze associations between RNs’ patient workload and level of involvement in direct patient care, their job satisfaction and intention to leave in these countries.A workforce survey was conducted through RN4CAST, an EU 7th framework project. The survey included 118 items derived from validated instruments or tested in prior research. Responses from 1133 RNs at 32 Finnish hospitals, 3752 RNs at 35 Norwegian hospitals, and 11 015 RNs at 71 Swedish hospitals comprise the database, which was analyzed using logistic and odds ratio regressions analyses.95% ≤ 1.32). Nearly half the Finnish sample report intention to leave, with significantly lower levels in Norway and Sweden (p < 0.001). Patient workload is associated with job satisfaction and intention to leave to some degree in all countries, i.e. greater patient workload, less job satisfaction and greater intention to leave.We found statistically significant differences in RNs’ level of involvement in direct patient care , in patient workload and in number of patients needing ADL assistance and surveillance. A U-formed relationship was found between level of involvement in direct patient care and intention to leave in Sweden, and more satisfaction among RNs in roles with more direct patient care (OR = 1.16, 1.02 ≤ CIThis study suggests that more attention paid to patient mix, workload and role of RNs in patient care might potentially diminish intention to leave and increase job satisfaction in these Nordic countries. The health care systems (HCS) in the Nordic countries share many features. Finland, Norway and Sweden all share principles of equal access to care, with health care primarily provided by the public sector and thus subject to government control. The HCSs in all three countries are largely full-coverage and tax-financed. In addition, these three countries have all undergone public reforms and changes in their respective HCS since the early 1990s to increase patient influence and economic efficiency .th framework RN4CAST project (Nurse Forecasting: Human Resources Planning in Nursing 2009 – 2011) [not recommend the hospital as workplace to other RNs was 26% in Sweden compared to 17% in Finland and 14% in Norway [Despite these similarities in HCSs, a comparative study based on European data from the EU (European union) 7 – 2011) ,4 indican Norway ). The coThe aim of this study is therefore to explore associations between RNs’ patient workload and level of involvement in direct patient care, and RNs’ job satisfaction and intention to leave their current hospital or the nursing profession in Finland, Norway and Sweden.Specific research questions addressed include:•Does RNs’ level of involvement in direct patient care, patient workload, job satisfaction, and intention to leave differ among the three Nordic countries?•Is RNs’ patient workload associated with job satisfaction and intention to leave?•Is RNs’ level of involvement in direct patient care associated with job satisfaction and intention to leave?•To what extent does workload interact with the RNs’ level of involvement in direct patient care with regard to job satisfaction and intention to leave?Investigating these issues among countries with such similar HCSs will both provide new knowledge of interest for policy decisions, and will serve to generate new hypotheses for further investigation in countries with different HCSs.In these three Nordic countries, in-hospital health care provision is the responsibility of regional authorities with hospitals providing specialized services for their catchment area; Finland has 20 hospital districts, Sweden has 21 health care authorities, and Norway has four. While specific health care financing mechanisms between different levels of government and individual hospitals vary, the out-of-pocket costs for hospital care for individuals in all three countries are <5% of the total cost . AnotherThree categories of nursing staff can be found in these Nordic countries, although in acute care settings, all have shifted to a predominance of RNs during the past few decades. RN education also is relatively comparable among the countries. All have undergone a shift from technical and non-academic to academically-based nursing education, which means that the RN workforce has an educational mix, resulting in an inverse relationship for many between length of experience and educational level. At present, RN education is provided at universities or university colleges, although in Finland it is a half year longer (210 ECTS - European Credit Transfer System points) than the three-year programs in Norway (180 ECTS) and Sweden (180 ECTS). Since the early part of this century these programs all lead to both a Bachelor Degree and RN licensure . AssistaBetween 80-90% of clinically-active RNs are unionized in Finland, Norway and Sweden , level of involvement in direct patient care (1 item) and intention to leave (2 items). Table To address the research questions posed here, nine items from the RN4CAST nurse survey questionnaire were analyzed. The items were categorized into four basic constructs: job satisfaction (2 items), ent care  item andPurposive, stratified sampling based on geographic location was used to select hospitals with >100 patient beds which provided acute care. This approach allowed recruitment of facilities in different hospital districts, in urban and rural areas including all the major cities, and different types of hospitals , with at least one university or central hospital selected from each district. The Helsinki-Uusimaa district, comprised of five hospitals and providing health care for 28% of the population, was an exception; recruitment was adapted here to allow for representativity in this area. Thirty-two of the 34 hospitals invited to participate in the study agreed; survey data was then collected from a minimum of two general medical/surgical units per hospital.The RN survey was conducted in Finland from November 2009-February 2010. A total of 2,463 RNs at 130 units were asked to participate by an email invitation sent to each RN at either a personal email address or to a hospital unit email, by either the researchers or the head nurse of their unit, depending on hospital policy. The email invitation contained a link to the web-based questionnaire. A first reminder was sent after one week, with most hospital units sent two reminders. Hospitals with RN response rates <40% were contacted again in February 2010, and asked to make further efforts to motivate RNs to respond to the survey. The final response rate per hospital varied from 27-62%.As national registration of up-to-date RN workplace information does not exist, the survey was distributed via the Norwegian Nurses’ Organization’s (NNO) local representatives at acute care hospitals with a minimum of 90 patient beds . They received 6,600 paper questionnaires with survey ID numbers distinguished by workplace, and distributed them to RN colleagues. Completed questionnaires were returned to the Norwegian Knowledge Centre for the Health Services. This procedure did not allow for systematic reminders to be distributed; instead the local NNO-representatives were asked to encourage and remind RNs to respond.The sample included all but one of the hospitals large enough for survey inclusion . The response rate per hospital varied from 24 to 86%.Sweden also collaborated with the union organizing RNs, the Swedish Association of Health Professionals, using their member register for recruitment to the RN survey. As the register contains self-reported workplace information on both hospital and department, but without information as to specific nursing function, all RNs registered as working in medical or surgical departments were included. Residential addresses were linked to this sample via Sweden’s system of individually unique national registration numbers. The survey was then posted in February 2010 to over 33,000 RNs’ residential addresses by Statistics Sweden, with the option of either returning it by pre-paid mail or completing a web-based version. Three reminders were sent at standard intervals, with the last two each containing a new copy of the survey. The return rate was just under 70%, with hospital response rates varying from 53- 81%.Final study inclusion was based on responses to the first question, designed to eliminate conscious over-recruitment, i.e. RNs not meeting study criteria by for example, not working actively with in-patient care, working in non-medical/surgical units, or in ICUs or operating rooms. The second question was to verify or change workplace data derived from the register; respondents who had changed workplace or did not otherwise meet inclusion criteria were also excluded.The recruitment strategies used in Sweden allowed a non-respondent bias analysis based on known background variables  to be performed, with no systematic differences detected between responders and non-responders. Since the material included a known over-recruitment, analysis of background variables was also conducted to establish if there were systematic differences in response rates between the study group and the over-recruitment group, without any systematic difference detected.age, experience, number of patients cared for on last shift) for the samples in each country were calculated. Country differences in the ordinal scale item, job satisfaction, were analyzed using Mann–Whitney U-test. Differences in proportions to verify differences between countries for other descriptive variables were analyzed using Chi2-tests. Proportion odds models were used to determine if involvement in direct patient care and workload were associated with job satisfaction, controlling for age, sex, length of work experience, hospital and reported number of nursing staff. In regression analyses, job satisfaction was operationalized using the item How satisfied are you with your current job in this hospital? as this item showed most variability. Logistic regression analyses were used to determine if involvement in direct patient care and workload were associated with the dichotomous outcome variable intention to leave current hospital (yes/no), controlling for age, sex, length of work experience, hospital and reported number of nursing staff. By controlling for hospital, we aimed to eliminate the effects of variability due to differences in work environments.  employed was 0.05 for all analyses.The project was approved by the relevant Research Ethics Committee in respective country . All participants were informed that the collected data would be analyzed in anonymous form and that participation was voluntary. Completing and returning the survey was considered a sign of informed consent by the ethical committees.The vast majority of the respondents in all countries were women; 6% were men in Norway and Sweden with the proportion in Finland significantly lower (3%). The Finnish sample was both statistically significantly older (mean age 41.7) and reported longer nursing experience (mean 14.1 years’ experience) than the participating RNs in Sweden  and Norway .More respondents in Norway (5%) than in the other countries  reported having received their basic nursing education outside the country, with 27% of these Norwegian respondents educated in Sweden and 7% in Finland. Twenty-seven percent of the RN respondents from the Swedish sample who reported a foreign nursing education were educated in Finland and 5% in Norway (data not shown). Approximately 1% of the Finnish and Norwegian RNs reported having worked in another Nordic country; 0.4% of the Swedish sample reported having had worked in Finland, whereas 9% had worked in Norway (data not shown).There are distinct statistically significant differences (p < 0.001) regarding the reports of the RN level of involvement in direct patient care between Sweden on one hand, and Norway and Finland on the other (see Table The mean number of patients the RNs reported being directly responsible for on the most recent shift differed significantly (p < 0.001) among all three countries, with Finnish nurses reporting about 40% more patients (9.4) compared to Norway (6.5) and Sweden (7.6) (see Table As shown in Table The distribution of responses to the question on job satisfaction differed significantly (p < 0.001) among countries  from the Swedish sample, in which 72% would recommend her/his hospital as a good workplace  of the RN respondents from Finland reported that, if possible, they would leave their current hospital within the next year due to job dissatisfaction, with approximately one-third of the Swedish sample and one-quarter of the Norwegian sample responding in this manner Table ; these d95% ≤ 1.4). This pattern is specific to the Swedish sample, and not found in either the Finnish nor Norwegian data.In the bivariate analyses (see Table 95% ≤ 1.5) and Sweden OR = 1.24 (1.19 ≤ CI95% ≤ 1.29), as well as the number of patients with ADL (Norway OR = 1.29 (1.22 ≤ CI95% ≤ 1.36) and Sweden OR =1.31 (1.27 ≤ CI95% ≤ 1.35)), and surveillance (Norway OR = 1.21 (1.14 ≤ CI95% ≤ 1.29) and Sweden OR = 1.13 (1.1 ≤ CI95% ≤ 1.16)), needs. This indicates that an increased number of patients in general as well as increased number of patients with ADL and surveillance needs is related to higher odds of work dissatisfaction. In the Finnish sample, the number of patients with ADL (OR = 1.07 (1 ≤ CI95% ≤ 1.13) and/or surveillance needs (OR = 1.11 (1.03 ≤ CI95% ≤ 1.19) is inversely related to job satisfaction, although no statistically significant association is found between job satisfaction and the overall number of patients the RN was responsible for on her/his most recent shift (OR = 1.08 (0.97 ≤ CI95% ≤ 1.19).In Sweden and Norway, job satisfaction is inversely related to the number of patients the RN reported being directly responsible for (Norway OR = 1.38 (1.27 ≤ CI95% ≤ 1.32)). In the Swedish sample, the effect of increased number of patients in general (OR = 1.07 (1.02 ≤ CI95% ≤ 1.13)), as well as increased number of patients with ADL needs (OR = 1.26 (1.22 ≤ CI95% ≤ 1.31)), remains statistically significant related to higher odds of work dissatisfaction. In the Norwegian sample, only the effect of an increased number of patients with ADL (OR = 1.2 (1.29 ≤ CI95% ≤ 1.11)) and surveillance needs (OR = 1.08 (1.16 ≤ CI95% ≤ 1.01)) on job satisfaction remains statistically significant in these controlled multivariate analyses, while no statistically significant effects remain in the Finnish sample.In Table 95% ≤ 0.96)). This indicates that the relationship between level of involvement in direct patient care and intention to leave is u-shaped, with higher odds ratios for intention to leave found both among respondents reporting that they provided most care themselves, and those reporting least direct patient care.In the bivariate analyses (see Table 95% ≤ 1.18), Norway OR = 1.4 (1.27 ≤ CI95% ≤ 1.55)), as well as increased number of patients with ADL (Sweden OR = 1.21 (1.17 ≤ CI95% ≤ 1.25), Norway OR = 1.26 (1.18 ≤ CI95% ≤ 1.33)) and surveillance needs (Sweden OR = 1.13 (1.1 ≤ CI95% ≤ 1.17), Norway OR = 1.2 (1.12 ≤ CI95% ≤ 1.28)). In the Finnish sample, the total number of patients the RN is responsible for is directly related to intention to leave (OR = 1.17 (1.05 ≤ CI95% ≤ 1.29)).As was the case for job satisfaction, in Sweden and Norway intention to leave one’s current hospital was found to be associated with increased number of patients the RN reported being directly responsible for (Sweden OR = 1.12 (1.08 ≤ CI95% ≤ 1.26), Sweden OR = 1.18 (1.13 ≤ CI95% ≤ 1.23)) and surveillance needs (Norway OR = 1.11 (1.02 ≤ CI95% ≤ 1.2), Sweden OR = 1.08 (1.04 ≤ CI95% ≤ 1.11)) remains statistically significant when controlled for the above-named variables. In the Finnish sample, no significant associations between these variables are found in the multivariate analyses.In the multivariate analyses . Wa , theIn summary, this article presents the first comprehensive analysis of issues related to organization of nursing care in these Nordic countries, with large data sets systematically collected to allow comparisons. Other comparative studies have not included nursing issues, in part because stringent and comparable data has been lacking to date  were actively involved in the design of the study presented here. CaT, RL, and SR were responsible for data collection in Sweden. AE,VJ and JK were responsible for the data collection in Finland and ISS, CT and MWT were responsible for the data collection in Norway. AE,VJ and JK actively participated in writing the portions of the article based on Finish health care and data, and ISS, CT and MWT for corresponding sections for Norway. RL led, with CaT, writing of the first and successive drafts of the paper. SR carried out statistical analyses and contributed to the writing of the manuscript. All authors contributed to interpretation of the results, commenting and revising the text. All authors approved of the final version.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6955/13/27/prepub


A popular model of the function of selective visual attention involves search where a single target is to be found among distractors. For many scenarios, a more realistic model involves search for multiple targets of various types, since natural tasks typically do not involve a single target. Here we present results from a novel multiple-target foraging paradigm. We compare finger foraging where observers cancel a set of predesignated targets by tapping them, to gaze foraging where observers cancel items by fixating them for 100 ms. During finger foraging, for most observers, there was a large difference between foraging based on a single feature, where observers switch easily between target types, and foraging based on a conjunction of features where observers tended to stick to one target type. The pattern was notably different during gaze foraging where these condition differences were smaller. Two conclusions follow: (a) The fact that a sizeable number of observers (in particular during gaze foraging) had little trouble switching between different target types raises challenges for many prominent theoretical accounts of visual attention and working memory. (b) While caveats must be noted for the comparison of gaze and finger foraging, the results suggest that selection mechanisms for gaze and pointing have different operational constraints. Your visual attention is tuned to items containing the relevant features, the size, sheen, and texture of the coins.Imagine yourself on a crowded sidewalk. You are about to feed coins into a parking meter. You drop your wallet and coins of various sizes and denominations scatter around you. You collect the coins as quickly as you can while avoiding irrelevant stimuli such as chewed gum, pieces of paper, and other debris. This is an example of a visual Visual attention enables us to select relevant items for processing . A populRecently, we introduced a new “finger foraging” task to explore the behavior of human participants when faced with search for multiple items from more than one target category . DirectlWhen individual targets were defined by a single feature , participants selected randomly from the two target categories. When target categories were defined as conjunctions of color and shape , most participants selected items in long “runs” of the same type . A “run”To our knowledge, this was the first demonstration of attention-modulated, run-like behavior in humans. The close parallel with animal studies, where foraging behavior can switch from random selection amongst all available sources when food is conspicuous to run-like behavior when it is cryptic , led us One of the central aims of this paper is to contrast foraging with fingers, as above, to foraging by gaze. A common conception of motor control is that the movement of eye or hand to a particular location involves a similar attention plan . ConsistBut other studies suggest that things are not this clear-cut. Another reason for comparing gaze and finger foraging is the hypothesized relation between eye movements and visual attention . While mWe had three main goals in the current study. First, using identical displays, we wanted to replicate the pattern of finger foraging seen in our previous study. Second, we wanted to extend these findings by examining a different response modality where observers canceled predesignated targets by fixating them for 100 ms. Our primary concern was whether run-like behavior would be observed with gaze foraging. We note that we did not attempt to fully equate the task parameters for the two modalities (see “Methods” section for details). Our primary focus, then, will be on within-modality patterns of foraging. However, we do provide both qualitative and quantitative across-modality comparisons for the sake of completeness. Comparing finger and gaze foraging in the same individuals could shed important light on the nature of the attentional constraints operating in the two versions of the task.Thirdly, we wanted to explore another aspect of our original data: the presence of clear individual differences in how attention constrains search. Specifically, 4 of our original 16 participants showed essentially no change in finger foraging behavior between feature and conjunction conditions. We termed them “super-foragers” as their continued random selection from both categories during conjunction search allowed them to complete the task more efficiently—that is with less overall movement—with only very modest time cost  participated. Five were excluded since their data were incomplete.Twenty-one naïve observers with normal or corrected to normal vision . Stimulus presentation and response collection were carried out with a custom iPad application written in Objective-C using the Xcode and Cocos2d libraries.For gaze foraging, a high-speed eye-tracker from Cambridge Research Systems tracked observers’ dominant eye at 250 Hz . Stimuli were displayed on a 100 Hz 19″ Hansol CRT screen (model: 920D resolution: 1024 × 768) controlled by a 2.33 GHz PC . Viewing distance was 60 cm (ensured with head rest). The experimental program was written in Matlab and functions from the Psychtoolbox  and the For feature foraging, the targets and distractors were either red or green disks among blue and yellow distractor disks, or vice versa see . For conbetween participants.For finger foraging, there were 40 targets and 40 distractors with a diameter of 20 pixels that were distributed across a 10 × 8 grid offset from the edges of the screen by 150 × 100 pixels with the viewing area occupying 15 × 12 cm. Minimum gaps between stimuli prevented overlap. For gaze foraging, there were 16 targets and 16 distractors. All had a diameter of 1°, distributed randomly across the screen but offset from its edges by 3.2°. If observers’ gaze fell within a square region of interest (ROI) surrounding each stimulus, they were considered to be fixating that stimulus. Note, importantly that the target ROI was 4° but 1° around the distractors to minimize accidental selection of distractors. The fixation time required for selection was 100 ms (after which the target disappeared). Feature versus conjunction foraging and gaze versus finger foraging were administered in counterbalanced order. Stimulus categories for the foraging types were consistently paired within participants and across foraging method although they were randomized and counterbalanced t tests.Our primary dependent measure was average run length on a given trial. Average run length is a good indicator of foraging strategy—short runs suggest random target selection, longer runs suggest attention-constrained foraging—and is simply computed by summing the length of consecutive choices of the same target and dividing by the total number of runs on a given trial. We compared within modality run length in the feature and conjunction conditions, averaged across-trial, with paired In addition to raw run length data, we also computed normalized scores to aid comparison across modalities. We subtracted individual trial averages from a grand mean, computed across both feature and conjunction conditions, dividing this value by the overall standard deviation, again computed across the two conditions. These normalized scores were compared using a 2  × 2  repeated measures analysis of variance (ANOVA). To assess whether observers showed differences between feature and conjunction foraging, we categorized them into same-pattern or different-pattern groups based on whether there was more than a one standard deviation difference between their standardized score for the two conditions. We also measured completion time, total movement length, and error rates. These remaining dependent variables are described in more detail in the relevant results sections.In a sound-proof booth, observers completed the tasks by fixating (or tapping) all targets while avoiding distractors. When a trial was completed, a message conveying successful completion appeared on the screen followed by the subsequent trial. If a distractor was selected, an error message appeared and a new trial started. Finger foraging was performed under normal illumination. Participants performed five practice trials and then had to complete 20 trials of each type correctly . During gaze foraging, the only lighting came from the computer monitors except that three participants required mild background lighting for accurate eye-tracking due to enlarged pupils in the dark. After calibration, participants performed 10 practice trials followed by the 20 experimental trials.t(15) = 6.19, p < .001, Cohen’s d = 1.75) during feature  than conjunction  foraging. Within this overall pattern, however, there are clear individual differences. Classifying observers in terms of the distance between their standardized scores in the two conditions, as described earlier, revealed that 11 participants showed consistent differences between feature and conjunction foraging, while 5 did not (see t(15) = 3.67, p = .002, Cohen’s d = 0.74) for feature  than conjunction foraging . However, as can be seen by comparing Similarly, F = 48.8, p < .001; ηpartial_squared = 0.76) and of foraging measure  = 21.0, p < .001; ηpartial_squared = 0.58) but also a highly significant interaction  = 23.6; p < .001; ηpartial_squared = 0.61). This is highlighted by comparing the number of participants classified as having the same or different patterns of foraging across feature and conjunction conditions. This difference in proportions between finger  and gaze  foraging clearly indicates that a larger number of participants continued to use random category selection when using their eyes.Below, we more directly compare performance in finger and gaze foraging. As already noted, such comparisons need to be interpreted with caution, given the methodological differences between the tasks, but are nonetheless useful as exploratory steps. First, we made a direct quantitative comparison on the normalized run length data. A 2 × 2 repeated measures ANOVA on run length revealed a significant main effect of condition  = 63.8; p < .001; ηpartial_squared = 0.81) of foraging measure  = 115.7; p < .001; ηpartial_squared = 0.89) and a significant interaction between the two factors  = 42; p < .001; ηpartial_squared = 0.74). Most importantly, these results show that there is very little nonrandom foraging with gaze while for conjunction foraging with fingers the majority of trials are nonrandom consistent with the fact that we see very long runs in that condition.Second, we computed the number of trials classified as nonrandom for each observer and compared these with a 2 × 2 ANOVA. To classify a trial as nonrandom, we used One-Sample Runs Tests with a Bonferroni correction to adjust the level of alpha for multiple tests  suggest that participants with similar feature and conjunction performance in the finger foraging task also have a tendency towards similar differences for gaze foraging  = 100, p < .001; ηpartial_squared = 0.87) and switching  = 52.4, p < .001; ηpartial_squared = 0.78) but not of foraging method  = 2.05, p = .17; ηpartial_squared = 0.12). The two-way interactions between condition and switch  = 45.2, p < .001; ηpartial_squared = 0.75), condition and foraging method  = 8.45, p = .011; ηpartial_squared = 0.36) and switch and foraging method  = 23.4, p < .001; ηpartial_squared = 0.61) were all significant. Finally, the three-way interaction was significant  = 17.8, p < .001; ηpartial_squared = 0.54), confirming that switch costs as a function of feature versus conjunction foraging differ between the two foraging methods.A three-way repeated measures ANOVA on response time switch costs  revealedF = 77.5, p < .001; ηpartial_squared = 0.838), of switching  = 24.7, p < .001; ηpartial_squared = 0.623), and of foraging method  = 41.1, p < .001; ηpartial_squared = 0.733). The two-way interactions between condition and foraging method, between condition and switch, and between foraging method and switch were all significant  = 16.3, p = .001, ηpartial_squared = 0.521; F = 39.6, p < .001, ηpartial_squared = 0.725; F = 36.4, p < .001, ηpartial_squared = 0.708, respectively). The three-way interaction was also significant  = 15.3, p = .001, ηpartial_squared = 0.505). The most notable result (highlighted in A three-way repeated measures ANOVA on switch costs in distance between consecutive taps (t(15) = 6.5, p < .001). This indicates that conjunction foraging was, on average, more difficult than feature foraging. The average traveling distance for each trial was 5346 pixels during feature foraging and 6436 for conjunction foraging (paired t(15) = 8.8, p < .001). This is not surprising since if observers use longer runs of foraging the same target, they will by necessity travel longer. The gaze foraging data mirror the finger foraging data: Average finishing time for each trial was 6.7 and 10.2 s for feature and conjunction foraging, respectively (paired t(15) = 6.9, p < .001). For feature foraging, the average traveling distance was 2922 and 3261 pixels for conjunction foraging (paired t(15) = 4.2, p < .001).For finger foraging, the average finishing time for each trial was 12.6 s for the feature condition and 17.6 s for the conjunction condition  = 42.3, p < .001, ηpartial_squared = 0.74) and of foraging method  = 12.1, p = .002, ηpartial_squared = 0.45) and a significant interaction  = 22.3, p < .001, ηpartial_squared = 0.60). A post-hoc test showed that the differences were always significant except between foraging methods in the feature condition. Overall, foraging appears to be highly organized during feature foraging, indicating that participants utilize consistent horizontal or vertical sweeps through the display when attentional load is low. Such tendencies are generally reduced during conjunction foraging, but the drop is much more marked for finger foraging. This differential pattern of organization is again suggestive that the conjunction manipulation has less of an impact on eye foraging than it does on finger foraging.Finally, we analyzed foraging organization. Calculating the correlation between the Cartesian coordinates of the targets and the sequence of how the targets are selected provides information on search organization . A high p = .426) in error-rates between feature  and conjunction  conditions were found for finger foraging, but for gaze foraging the error-rates in the conjunction task  were significantly higher (p < .001) than in the feature task . While significant, a difference of only 6 percentage points is unlikely to account for the strong difference in the observed foraging patterns. Specifically, there was little or no nonrandom gaze foraging while for finger foraging the majority of trials were nonrandom. If the gaze pattern reflected a speed–accuracy trade-off, the difference in error rates should be far larger.Error-rates were defined as the proportion of total number of incorrect taps or fixations divided by the total number of targets. Using Wilcoxon signed-rank tests, no significant differences (We replicated our previous results on finger foraging  where moOur main new finding is that during gaze foraging, the proportion of observers who continue to switch categories is much higher than for finger foraging. While we did observe some run-like behavior for gaze foraging, very few trials across all participants were classified as nonrandom , in compThat gaze foraging is less constrained by the conjunction manipulation than finger foraging, is further supported by lower switch costs between target types and patterns of search organization. This may indicate that observers are simply more adept at switching between templates when eye gaze is involved, perhaps because eye gaze is a more basic, less complex behavior than finger movement e.g., , and maywithin a given modality, “super-foragers” allocate less resources to achieve precise localization, thus allowing them to more easily switch target categories under conjunction conditions. Our current data do not allow us to directly explore the link between target selection criteria and run-like behavior, but this is clearly an interesting avenue for future studies.Such basic differences between the two modalities could also constrain the accuracy of individual target selection events, which in turn could influence the overall pattern of run-like behavior. If participants need to allocate more resources to localize accurately when using the fingers, they may have less available capacity to aid in category switching. More generally, individual target selection criteria could interact with our more global attentional manipulation. For example, one possibility is that Finally, we note again, that the overall differences between our gaze-dependent and iPad displays, together with the possible target selection criteria issues just mentioned, made it impossible to completely equate gaze tasks and finger tasks in the current study. A caveat must therefore be noted regarding our findings, and the conclusions here need to be verified in future research. We believe, however, that such superficial differences in display and task parameters are unlikely to completely account for the lack of overlap between the two foraging modalities.Critical aspects of the current results raise problems for prominent theoretical accounts of attention. This is true for the finger foraging results, but even more so for the results from the gaze foraging task. A longstanding debate in the literature on visual attention involves the nature of WM representations and how they guide attention . A commoThe Theory of Visual Attention  may fareResearch on priming of attention shifts  shows hoOur results reveal both similarities and differences in foraging behavior across finger and gaze foraging. The results lend only partial support to proposals of overlap of eye movement control, motor control, and visual attention. All observers could switch easily between different target types during feature foraging, but many could also do this during conjunction foraging without notable costs, in particular during gaze foraging. Such rapid switching runs counter to key predictions of many theories of visual attention and WM.