Never before has the need for a modernized electronic record supporting Veterans’ health been clearer. A common, interoperable platform that seamlessly connects records from military service to VA is essential to our mission of caring for those who have served in our nation’s military and for their families, caregivers, and survivors. Such a platform is critical to VA becoming a high-reliability organization that ensures enterprise-wide consistency in clinical practice. It is also fundamental to our role as a learning health system that informs US health care overall.1As seen in the private sector, electronic health record (EHR) deployment, even under the best of circumstances, is rarely seamless. The fact that VA began its transition to a new system 7 months into a global pandemic added an unprecedented layer of complexity. Additionally, unlike deployments in the private sector, this transition occurred under the public microscope and with a high level of transparency. Together, these two recurring components of quality improvement—accountability and transparency—have been foundational to VA’s history as a learning health care system.Another chapter in that history, as noted by several papers, has been the evolution of the VA research program to promote actionable inquiry on ongoing changes in VA healthcare. This type of inquiry can help assure that successive EHR deployments are enhanced through shared learning and subsequent improvements. The nation’s emergence from the pandemic, as well as new priorities such as the PACT Act,As further context for these, as well as other articles, this commentary provides a synopsis of VA’s longstanding EHR work, lessons learned from initial deployment, and research questions that VA is poised to address.VA’s development of an EHR dates back to the 1970s; the journey over the subsequent 40 years is emblematic of VA’s forward-leaning approach to Veteran care and longstanding history in innovation. What began as a series of collaborations among VA, the US Public Health Service, the Department of Defense (DoD), and the Indian Health Service culminated in the 1978 launch of initial modules of an electronic information system at 20 VA medical centers.Less than 20 years later, then VA Under Secretary for Health Dr. Kenneth Kizer committed the Department to implementation across the national VA enterprise, christening the system “VistA” . Because the launch coincided with VA’s expansion to include both outpatient and inpatient care, VistA encompassed both modes of delivery from the outset.Since then, a common national EHR platform has allowed VA to share patient information efficiently, track system performance, and drive quality improvement. Notably, VistA/Computerized Patient Record System (CPRS) has been popular with VA clinicians, likely reflecting their extensive involvement in its development .However, as VA’s EHR began to take hold nationwide, the same capability that allowed individual facilities to customize worked against the ability to scale new innovations in health information technology and promote common clinical care practices. In other words, VA did not have one medical record—it had over 130 versions of a common platform reflecting local customization.Despite a decade of collaboration, the goal of creating a common interoperable record between VA and DoD remained out of reach. Consequently, in 2017 VA announced the Department would transition from VistA/CPRS to a commercial off-the-shelf EHR platform, selecting the same system DoD had earlier competitively selected for its 490 health care facilities in 2015.As outlined in the agreement, VA’s transition toward a new EHR at its 1454 medical centers and outpatient clinics would be the nation’s largest and most ambitious implementation. Over a 10-year phased rollout, the implementation would seek to address myriad, inter-related issues that also challenged private sector health care.2 These issues can involve problems in software design and clinician confusion.For example, a well-known implementation challenge is the potential for health information systems to cause errors, not just prevent them.3 Since then, most research has focused on documenting the impact of converting to a new record. Issues such as unintended effects on workload, communication among providers, and efficiency as well as the doctor-patient relationship and quality of care5 have yet to be fully explored.Furthermore, while the benefits of moving from a paper-based system to EHR were established more than a quarter century ago, the evidence-base regarding certain critical areas has been slower to develop. In its 1997 report, the Institute of Medicine discussed benefits such as improving quality, productivity, research, and data confidentiality.Moreover, research has yet to document the challenge of ensuring a safe and satisfying transition from one EHR to another. Since no vendor has ever undertaken a task of this size, it was assumed there would be numerous unanswered questions about implementing a new EHR in VA’s variety of environments. In anticipation of these issues, and with a goal of addressing the research gaps, the Health Services Research and Development Service within VA’s Office of Research and Development convened a meeting early in the process in 2019 with the VA Office of Electronic Health Record Modernization. Together, these offices mapped out possible areas where research could help with what was anticipated to be a large, complex, and challenging task.In October 2020, VA began implementation of its new EHR at several sites. At the same time, the global pandemic was in its relative infancy with cases surging, health care workers experiencing extreme stresses, and supply chains being disrupted. With both VA employees and EHR trainers also becoming infected, implementation assistance was sharply limited.In addition to training, other challenges emerged, such as lost consults and incomplete data migration. Many of these issues were documented in Office of Inspector General and General Accounting Office reports and through Congressional oversight. At the same time, VA maintained a level of transparency that enabled it to quickly respond to these and other concerns by conducting a patient safety review. VA also initiated a “sprint” team to assess required fixes to patient safety issues. While the platform was adapted to needs based on recommendations by a network of VA clinical councils, the scale of implementation meant it could not be further customized based on experience of frontline clinicians as it was rolled out. As a result, VA announced a strategic “reset” in early 2023 that indefinitely paused further new implementation until initial implementation issues could be examined and addressed.JGIM demonstrates, VA’s research program includes nationally recognized experts on EHRs and patient safety. Moving forward, their expertise, in collaboration with other VA national program offices and frontline staff, will enable us to leverage lessons learned from initial deployment.As this special issue of To start, there are three specific areas where the type of deep analysis allowed by research can contribute to findings of traditional Patient Safety and IT sprint teams. First, we need to deepen our understanding of patient safety issues by developing a more systematic set of safety monitors and metrics that can be assessed proactively, rather than reactively.Second, we need a stronger assessment of user experience and the role of training, with the aim of identifying the most valuable elements of training. This assessment must include identifying reliable approaches to minimize impacts on clinicians’ resilience. Already, researchers have begun to articulate mitigating factors, including the impact of local leadership engagement.Third, we need to fully understand the implementation’s effect on provider productivity. This will help sites deploying the EHR plan for additional support during initial startup while clinicians spend time adjusting to the new system.Beyond research, we must also recognize that new technologies and trends—artificial intelligence, patient-generated data, predictive analytics, and precision medicine to name a few—mean we must remain tightly focused on the EHR’s primary function: helping clinicians guide patients to the right treatment at the right time. Developing and scaling these innovations will be much easier with a single, consistent EHR platform. Moreover, innovations may help address “death by 1,000 clicks,” thereby allowing clinicians to spend more time listening to patients and less time at their keyboard. Finally, having more data will unlock new research avenues.While the need for a new EHR is clear, and the potential advantages compelling, our first commitment is to Veterans and the dedicated clinicians caring for them. Advances in information and communications technologies demand that EHRs develop capabilities that, at a minimum, do not preclude continued learning and improvement.The initial launch of a new EHR during COVID-19 frequently made it feel as if we were still building the plane while flying it. Today, as we emerge from a pandemic that revealed health inequities and left many physicians feeling burned out, we have recommitted to getting things right for patients and providers before proceeding further. As the papers in the special edition show, VA is actively using lessons learned from initial EHRM deployment to ensure that future rollout is safe, secure, and seamless for all.


We present an automatically generated dataset of 15,755 records that were extracted from 47,357 papers. These records contain water-splitting activity in the presence of certain photocatalysts, along with additional information about the chemical reaction conditions under which this activity was recorded. These conditions include any co-catalysts and additives that were present during water splitting, the length of time for which the photocatalytic experiment was conducted, and the type of light source used, including its wavelength. Despite the text extraction of such a wide range of chemical reaction attributes, the dataset afforded good precision (71.2%) and recall (36.3%). These figures-of-merit were calculated based on a random sample of open-access papers from the corpus. Mining such a complex set of attributes required the development of novel techniques in knowledge extraction and interdependency resolution, leveraging inter- and intra-sentence relations, which are also described in this paper. We present a new version (version 2.2) of the chemistry-aware text-mining toolkit ChemDataExtractor, in which these new techniques are included. Yet, it can remain difficult to model and predict novel photocatalysts. Past efforts3 have focussed primarily on electronic-structure calculations that employ density-functional theory (DFT), building off datasets that contain a large number of DFT results such as the Materials Project dataset4.Photocatalytic water splitting is an active field of research since the production of hydrogen from sunlight and water offers a promising solution for environmentally friendly energy. As a result, research into this field has exploded in recent years and there is an interest in applying machine learning to this field5. However, these catalytic-efficiency metrics alone are often not enough to discern the effectiveness of a photocatalyst given that catalytic activity can vary wildly based on the chemical reaction conditions under which the water-splitting reaction takes place. These conditions can include the light source used, the chemical additives that may be present, and even the photoreactor configuration7. To take these environmental conditions into account, the dataset introduced in this paper also includes the co-catalyst, any chemical additives that are present in the mixture, the length of time for which the experiment was conducted, and the type of light source used, including its wavelength.This study takes a different approach by developing a dataset containing experimental data that includes commonly reported key photocatalytic reaction attributes and their associated catalytic-efficiency metrics. These metrics were chosen from the academic literature, and include the solar-to-hydrogen efficiency (STH), the apparent quantum yield (AQY), and the amount of hydrogen emitted per unit of time or per unit amount of photocatalystThe extraction of such a large number of chemical reaction attributes presents difficulties for text mining. Firstly, multiple catalytic activities for multiple compounds are often listed in a single document, or at times, even in a single sentence, complicating the association between photocatalysts and the water-splitting activities in their presence. Secondly, the photocatalytic activity and the associated environmental conditions during water splitting are often presented across multiple sentences.Therefore, all HOCN samples exhibit enhanced photocatalytic H2evolution rates undervisible light irradiation (λ > 420 nm). In particular, HOCN4produced a remarkable enhanced photocatalytic H2production rate of1140 μmol h−1g−1under visible light irradiation, which is much higher than that of pureg-C3N4 . Further, HOCN4exhibits a high H2production rate of477 and 91 μmol h−1g−1under light illumination with longer wavelengths  […] A300 W xenon lamp  was used as the light source.”8.As an example of these challenges, consider the following passage, where the relevant data that we wish to extract and appropriately associate are highlighted. “https://github.com/CambridgeMolecularEngineering/chemdataextractor2, and the the specific version and additional code used for this paper have been made available on Figshare9. The Figshare also contains the data extracted in both normalised and denormalised JSON formats, and they have also been converted to a CSV format, as well as the hand-annotated test dataset and a separate hand-annotated dataset used during development in the same formats. These can be used for the purpose of photocatalysis property prediction or for evaluating any new data-extraction methods in this domain of chemistry.This paper presents novel techniques for the knowledge extraction of these distributed photocatalytic data and new interdependency-resolution techniques that allow the data to be appropriately associated during the text-mining process. Application of these newly developed techniques on a corpus of 47,357 papers resulted in a photocatalysis dataset with 15,755 records. Checking the validity of these data records using a subset of these papers revealed a precision of 71.2% and a recall of 36.3%. ChemDataExtractor has been made available at The corpus was gathered using the web-scraping capabilities included with ChemDataExtractor. Two search queries, “photocatalytic water splitting activity” and “photocatalyst hydrogen water activity”, were used to scrape articles published by the Royal Society of Chemistry and Elsevier, with the permission of the publishers. These scraped papers were then filtered based on whether the full-text articles were available in structured HTML/XML formats. This procedure resulted in a total of 47,357 papers being gathered for this work, consisting of 35,753 papers published by Elsevier and 11,604 published by the Royal Society of Chemistry.7 necessitated the construction of a complex knowledge representation for this study. This was a good match for the highly flexible knowledge-representation capabilities of the text-mining toolkit ChemDataExtractor version 2, which allows data extraction of nested models where there are complex relations between properties10. The model constructed within this framework contained the photocatalytic activity itself, as well as the experimental conditions under which the photocatalytic reaction took place, as illustrated in Fig. The highly variable nature of photocatalytic water-splitting activity depending on the chemical reaction conditions11 in units of μmol g−1, others12 as μmol h−1, and some others13 as μmol g−1 h−1. With regards to the environmental conditions, we chose those that seemed to be most commonly reported and most influential on the catalytic activity, while being feasible to extract and represent; namely, the wavelength of the light source, the type of light source, the power of the light source, co-catalysts used, and any chemical additives used, such as sacrificial reagents. While influential to the photocatalytic activity, some conditions, such as the reactor configuration, were not extracted in this work due to a lack of suitable quantitative knowledge representation that would be useful in downstream tasks, and others, such as the relative amount of co-catalyst, were omitted due to the diverse ways in which they could be represented.We chose three different measurement metrics to describe for photocatalytic activity: the solar-to-hydrogen efficiency (STH), the apparent quantum yield (AQY), and the amount of hydrogen emitted per unit of time or per unit amount of photocatalyst present in the chemical reaction. The first two measures are expressed as percentages, while the amount of hydrogen emitted is expressed differently depending on the paper, with some of them expressing this metricFurther, HOCN4 exhibits a high H2 production rate of 477 and 91 μmol h−1g−1under light illumination with longer wavelengths…) as a successful system would need to not only extract data from multiple parts of the sentence but also infer the association between data.While the complex nature of this knowledge representation was necessary in order to reflect the number of variables that affect photocatalytic activity, this resulted in a variety of challenges. Considering again the example passage given above, there are two key features that make the extraction of its photocatalysis-related information difficult. Firstly, there are numerous pieces of photocatalysis-relevant information within each sentence. It is particularly challenging to extract information from the second sentence  by evaluating the probabilities of sentences where each potential conjunct is used, and picking the conjunct that yields the highest probability14.Language models have been used to perform this task of identifying conjuncts  and take the simpler path of identifying the conjuncts by finding subtrees in the syntactic dependencies that are found for the sentence, originating at the words which are given the conjunct relation.We instead take advantage of the recently improved precision of syntactic dependency parsing using neural methods –N-TiO2 photocatalyst prepared from N-TiO2 by “one-pot” photodeposition of copper in the suspension showed a high activity in the in situ hydrogen generation under near-UV/visible light with glycerol in water, up to 675 μmol g−1h−1”18. Here, we cannot use the previous algorithm to extract clauses, but there are many chemical entities (Cu(0)–N-TiO2, N-TiO2) that could be associated with the activity value (675 μmol g−1 h−1).While clause extraction can be helpful, information extraction would still be difficult as even “basic” sentences with no coordinations can still contain multiple bits of information, and simpler automatically generated parsers can succeed at identifying the spans of text containing facts, but fail to correctly associate information. For example, take the following sentence: We introduce the Dependency-based Information Extraction (DepIE) algorithm, which aims to address this task of associating the correct spans with each other. An example of the application of the algorithm to the above sentence can be seen in Fig. μmol g−1 h−1 in the example above), as the “key value”.The algorithm takes as input a “key field”. The key field is defined as the field within the knowledge representation for which the identified spans will be most accurate, and which are necessary for a record to exist in a sentence. In the case of photocatalytic activities, we use the activity value (i.e. 675 μmol g−1 h−1 or Cu(0)–N-TiO2 in the example sentence above) and the syntactic dependency graph for the sentence. In practice, these spans of text that contain relevant information are either supplied by hand-crafted rules , or via the named-entity-recognition capabilities of ChemDataExtractor19 . The syntactic dependency graph is supplied by Stanza16 in our implementation.The first step of the DepIE algorithm is to identify all spans that contain chemically relevant information in a sentence . The second term is the probability of associating the correct spans with a random guess, while the first term is a rough approximation for the probability of associating the correct spans using DepIE. The first term is based on the intuition that if Dmin was equal to Dndsmallest2, then we would be effectively randomly guessing, while if Dmin much smaller than Dndsmallest2, we would expect the confidence to be close to unity. We then take the maximum between the two terms, with the assumption that DepIE’s guess will be better than a random guess. This confidence estimate is used to help prioritise merging of better quality extracted information, as detailed in the next section.The DepIE algorithm also associates a confidence with each association of spans, which is defined as20 that use more specific relations, limiting generalisability, or Open Information Extraction systems such as ClauseIE21, that do not focus on the Knowledge Base Construction of specific properties based on a user-defined schema as we do here.The novelty of this algorithm lies in its generalisability to the association of different user-specified properties by composing arbitrary recognisers for each of the fields, which can range from simple ones for property names to more complex ones for named-entity recognition. This permits this approach to be used not only to extract photocatalytic-activity values, but also to recognise the correct additives or co-catalysts. This open-endedness lies in comparison to approaches such as RelExWhile clause extraction and DepIE can extract knowledge well from individual sentences, in the case of photocatalytic water splitting, owing to the number of reaction attributes involved, it is not often the case that all pertinent information will be contained in one sentence. For this reason, we need to merge information from across different sentences.22. In this work, we extend this heuristic in three ways.Intuitively, using text proximity as a heuristic for prioritising merging seems to be a good idea, and ChemDataExtractor has always exploited thisFirst, we allow limits to be imposed on the ranges of text across which information can be merged, as it is not so likely that related facts be mentioned at opposing ends of a paper, although it does happen. Take, for example, the case of experimental method and results sections. These may be very far apart spatially in a paper, but the information contained in one section is highly likely to be pertinent to the other. To account for this eventuality, we allow the manual definition of closely related sections and treat them as though they are neighbouring sections, thereby allowing relevant information to be merged in even when the sections are far away in the document.Finally, we take into account the quality of the extracted information when merging data  by ordering candidate data for merging using a score that is defined as follows to better surface high-quality records:Review papers are much longer than regular research articles, and their content consists of a survey of key results from many such articles. They therefore contain data about many independent photocatalytic reactions, which are scattered throughout the paper. Review papers thus pose difficulties in our information-extraction pipeline. For example, reaction conditions from different experiments would be merged where they should not be. The use of review papers in data extraction would also have a disproportionate effect on the overall precision of the resulting dataset, because not only is it hard to extract the correct data from them, they also contain many records to extract incorrectly. Review papers were therefore filtered out from our data-extraction process, and we extracted data from only regular papers, which tended to exclusively describe one photocatalytic chemical reaction. For the same reason, certain journals , which are either off topic or seem to encourage the reporting of many results about multiple chemical reactions, were also filtered out.“We obtained the utmost hydrogen evolution i.e., 10200 μmol h−1g−1for the naked ZAS  catalyst under visible light, which is much higher than the earlier reported photocatalysts.”23 This sentence describes the photodecomposition of H2S, but on its own, it is indistinguishable from a sentence about photocatalytic water splitting.Furthermore, a labelled dataset, that was manually created for validating the pipeline during development, showed that the results of scraping involved many papers where photocatalysis was only mentioned in passing. Such papers that simply mention the topic of photocatalysis would harm precision greatly as many of the sentences contained within them would, on their own, be indistinguishable, even for a human, from a sentence that would be in an “in-topic” paper such that it would produce incorrect records. An example of such a sentence is 24 would enable the distinction of such types of text, such modelling would require a large corpus of papers. Instead, we found that filtering based on the title, using simple block/allow lists, was enough to remove any “out-of-topic” papers in the validation dataset, and this also generalised well to the test dataset.Although topic modellingFinally, certain sections of some papers were also prone to contain disproportionately more false positive records than true positives. For example, many papers contain sections on characterising or synthesising the photocatalysts, which confuse the merging step in the information-extraction process since these would also often mention light sources or reaction times. A filter was implemented to address the issue, whereby sections or sentences which seemed irrelevant to the topic would be skipped. The sections that would be skipped were also chosen manually based on the development dataset, and this generalised well to the test dataset.9.While the DepIE parsers were generated from the knowledge representation in this data-extraction process, certain parts of the pipeline had to be hand tuned, based on findings from use on a small development dataset that comprises 30 randomly selected, hand-labelled papers, and these annotations have been made publicly availableThe first thing that had to be tuned here was the filter, where the keywords used to identify relevant sections and documents were picked. Related sections and document-text range limits for merging that worked well for the corpus were also identified and added to the pipeline. Finally, the dimensionalities of the photocatalytic properties and their names, including their synonyms where relevant, were ascertained for the knowledge representation so that the automatically generated parsers could function well.9, in both normalised and normalised JSON forms. The normalised form can be interpreted by any standard JSON reader, while the normalised form can be read using the CDEDatabase (https://github.com/ti250/cdedatabase) library to create Python objects where the user can access more features such as standardised values.The hand-annotated data and extracted data are all available online on FigshareApparentQuantumYield, and the fields are as described in Fig. In either case, the filename contains the type of the record, e.g. To evaluate the dataset produced, a random sample of 330 open-access papers was taken from the overall dataset, with 220 of these papers being published by Elsevier and 110 papers being published by the Royal Society of Chemistry. These papers were then annotated by hand to create a test dataset. We computed precision and recall both for exact matches and relaxed matches.For an exact match, the extracted value must precisely match the way that this was expressed in the original paper. This was particularly problematic in the case of chemical-entity names owing to the importance of structure and loading in photocatalysis. For example, an extraction of just “CDSe” would not be an exact match for “CDSe QDs” and similarly, “TiO2” would not be a match for “Ni(OH)2-decorated TiO2”. These partial matches are still useful extraction results which add only correct information, albeit incomplete information. To account for these cases of incomplete information, we also measure precision and recall with relaxed matches, and report these alongside each other. The results can be seen in Table feature of how the data are distributed within these papers. The high precision of 71.2%, despite this distribution, shows that our method effectively filters out false positives from these papers.It was found that a large proportion of the papers in this test set actually did not contain any records. In fact, 90% of the papers in the test set did not have any records at all. As Fig. n papers used to calculate the precision and recall would change the results; so we also calculated a standard deviation on the metrics based on 10 separate samples of n papers. While this gives a bad estimate as n approaches 330, due to all the samples containing the same papers, it nevertheless provides an additional illustration of whether or not the dataset size was big enough to accurately judge precision and recall.We also evaluated whether the dataset was large enough or not, by evaluating the convergence of the precision and recall metrics with the number of papers labelled, as shown in Fig. Another thing to note in the results is that our system did not perform as well at extracting light-source filters and names, where the recall is much lower than with any other field  to create Python objects. A converted CSV version of the data is also available in the respective repositories. Documentation and sample code to read either format is also provided on Figshare, alongside the scripts used to create the figures in this paper. The extracted dataset could be used for photocatalytic-material discovery and the hand-annotated dataset could be used for the evaluation of future efforts in chemical-information extraction pipelines to provide insights into the use of NLP to mine textual data from synthetic chemistry.Both the extracted data and the hand-annotated data are available on Figshare


Music emotion representation learning forms the foundation of user emotion recognition, addressing the challenges posed by the vast volume of digital music data and the scarcity of emotion annotation data. This article introduces a novel music emotion representation model, leveraging the nonnegative matrix factorization algorithm (NMF) to derive emotional embeddings of music by utilizing user-generated listening lists and emotional labels. This approach facilitates emotion recognition by positioning music within the emotional space. Furthermore, a dedicated music emotion recognition algorithm is formulated, alongside the proposal of a user emotion recognition model, which employs similarity-weighted calculations to obtain user emotion representations. Experimental findings demonstrate the method’s convergence after a mere 400 iterations, yielding a remarkable 47.62% increase in F1 value across all emotion classes. In practical testing scenarios, the comprehensive accuracy rate of user emotion recognition attains an impressive 52.7%, effectively discerning emotions within seven emotion categories and accurately identifying users’ emotional states. Music serves as a powerful medium for expressing and evoking human emotions. As our understanding of user emotions continues to evolve, it has become imperative to develop more sophisticated and personalized emotion identification technology. However, since emotions are inherently subjective, many users refrain from expressing their emotions on social media, struggle to articulate their feelings accurately, or may not even be fully aware of their emotional states. Consequently, directly obtaining users’ emotions presents a significant challenge. In response to this challenge, researchers have explored the notion that music can effectively convey and elicit emotions. Thus, organizing and retrieving music based on its emotional attributes has emerged as an objective approach. However, compared to other music concept recognition tasks, emotion recognition in music remains in its nascent stages. This is partly because the expression of music is subjective and challenging to quantify. Additionally, music emotion transcends mere audio data and cannot be comprehensively recognized solely based on audio features.In the realm of music information retrieval, researchers have experimented with diverse music features to classify music emotions. Some studies have combined audio and text features , while oCurrently, music emotion recognition employs feature selection strategies that typically involve selecting a set of frequency domain and time domain features based on researchers’ experience. Alternatively, feature selection algorithms may be utilized to identify features from a broader range of frequency and time domain sets . HoweverIn essence, the emotional attributes present in the song list can be inferred to characterize the emotions associated with each individual music track. This indirect approach enables researchers to estimate the emotional content of music without requiring explicit emotional annotations for each individual song, thus circumventing the direct annotation challenge and opening new possibilities for advancing music emotion recognition methodologies.Matrix decomposition techniques can effectively reduce the dimensionality of the data. In the context of music emotion representation and user emotion recognition, this can be particularly valuable, as it helps capture the most relevant features of the data while eliminating noise or irrelevant information. Matrix decomposition can reveal latent features in the data that are not directly observable in the original representation. In music emotion analysis, these latent features could correspond to underlying patterns related to emotions. By extracting these features, the algorithm may achieve a more concise and expressive representation of emotional content in music. Therefore, the main goal of this article is to learn the representation of music in emotional space with the help of nonnegative matrix factorization (NMF) integrating external information, and based on user’s listening list data and music emotion recognition architecture, a new user emotion recognition algorithm is designed, which can effectively identify the user’s emotion in a short period of time.At first, music emotion classification is based on audio features, but the effect of music emotion recognition only based on audio features is not ideal. With the development of multimodal information fusion technology, more and more scholars use the idea of multimodality to retrieve music information. “Multimodal fusion” is the combination of different information sources and helps the system to complete specific tasks through the collaborative use of information provided by multiple modes . ResearcIn the field of music information retrieval, more and more researchers combine music emotion with user emotion to carry out research. Music is not only emotional in itself, but also evokes the emotions of the audience . The abiNon-negative matrix decomposition (NMF) combines cryptic meaning and machine learning characteristics, which can dig deeper connections between users and items. Therefore, the prediction accuracy is relatively high, and the prediction accuracy is higher than neighborhood based collaborative filtering and content-based recommendation algorithm. The model can be trained by stochastic gradient descent method and alternating least square method. At the same time, matrix decomposition has low time and space complexity. The mapping of high-dimensional matrix into two low-dimensional matrices saves storage space, and the training process is time-consuming, but can be completed offline. Score prediction is generally calculated online, directly using the parameters obtained from offline training, which can be recommended in real time.The NMF technology is specifically explained. The main problem of the model is transformed into the structure shown in The co-occurrence information is represented as a two-dimensional matrix. This type of matrix is a grid-like structure with rows and columns, forming a table. In this context, the rows correspond to different song lists, and the columns correspond to individual musical pieces. The elements of the matrix represent the co-occurrence strength or count of each musical piece in each song list. For example, if element  of the matrix is 3, it means that the j-th musical piece appears three times in the i-th song list.By using this two-dimensional matrix to represent the co-occurrence information, researchers, data analysts, or music enthusiasts can analyze patterns and relationships between different song lists and musical pieces. It allows them to discover which songs are commonly associated with specific playlists or which playlists share similar songs, thus aiding in music recommendation systems, playlist generation, and other music-related data analyses. The co-occurrence information of playlist and music is represented as a two-dimensional matrix, in which each row represents a playlist and each column represents a piece of music. The value in the matrix is 0 or 1, indicating whether the corresponding music appears in the playlist . Due to the large number of music in the list, there will be problems such as excessive resource consumption and time-consuming in data calculation. The form of matrix X should be preprocessed as follows:where m represents the singular number of songs and n represents the number of musicT. as shown in A two non-negative factorization matrices of the X should be arranged to replace U and V, which make the original matrix X as close as possible to the factorized result UVIn the context of music emotion representation, the song sheet or music emotional label information which is labeled by experts is included in the matrix decomposition as external information. Assuming that the following objective functions should be minimized as follows:0 represents music-emotion label matrix, Gv is the diagonal indicator matrix representing the musical level. Gv  = 1 indicates that the I track contains an emotional indicator, and Gv  = 0 if it does not.Among them, Vi, then music i can be expressed in the k-dimensional emotional space as:The quintessence of music feeling acknowledgment is the portrayal of music in the profound space. Let the emotional distribution of music i be Vi corresponding to music i is normalized, and the emotional probability distribution The Vwhere k represents the dimension of emotional space.For each piece of music, the highest emotion category is taken. The calculation formula is as follows:i, and the emotion distribution vector of the j-th neighbor of music i is Through the above calculation, the distance relationship between music and music is obtained. The first k music most similar to music i is regarded as the neighbor of music i, which is represented by N(i). Assume that the emotion representation vector of unknown music i is sFinally, the emotional category of music with the maximum value is selected as the result of emotional classification of music, and the emotional category to which music i ultimately belongs is s The music that users have listened to forms the music list. In order to realize the recognition of users’ immediate emotions, the emotion of each music in the music list should be identified first. Combined with the user’s historical listening behavior, an emotion recognition algorithm is designed to calculate the user’s emotional perception when listening to songs. Finally, the emotion of users listening to songs is classified into “cure, relaxing, romantic, nostalgic, excited, lonely and quiet”.  The proposed framework encompasses three main components: Melody Compilation, Sentiment Elicitation, and Affective Computation. Firstly, in the Melody Compilation phase, it is essential to acquire historical user data from their music-listening experiences. Generally, users create a song compilation, serving as a curated list of songs. Secondly, in the Sentiment Elicitation phase, a specialized algorithm for music sentiment extraction is designed to capture all emotions embedded in the song compilation, whether overt or latent. Lastly, during the Affective Computation stage, the acquired music sentiments are utilized to compute the user’s emotional representation based on their music compilation. This computation yields distinct emotional categories, thereby accomplishing the objective of emotion recognition. Ultimately, the outcomes of user emotion recognition can be applied to various specific scenarios.First, the user emotion recognition is transformed into a mathematical problem. Let the emotion distribution vector of the i-th user be wj, and the weight calculation method is as follows: If music ranks first, that is, the number of times users listen to songs is the largest, then the weight of music is 100; While if the music is not ranked first, the corresponding weight = the number of times the music is listened to/the times music ranked first is listened to ×100Based on the user’s listening list, the number of times the user listens to songs is taken as the weight, and the weighted average value of the emotional vector of music in the historical listening list represents the user’s emotions, and the user’s emotional distribution is finally obtained. The corresponding weight of song j in the listening list is set as Assuming that the distribution vector of music emotion is The calculation formula of user i’s emotional representation is as follows:After obtaining the emotional distribution vector of the user, the emotional dimension of the maximum value is taken as the final emotion of the user. The final emotional class of user i is expressed as follows:The dataset utilized in this research is derived from the “Music Emotion Dataset”. It comprises user-generated song lists, along with corresponding label data assigned by users. The dataset encompasses a total of 13,565 song lists, encompassing more than 490,000 individual songs. Each song list is associated with five distinct categories of tags, namely language, style, scene, emotion, and theme.For the experimentation, a subset of 4,200 music pieces is selected as a sample. Within this sample, 4,927 songs are designated as the feature space, with each song represented as a music vector that characterizes its distribution across the attributes of the song list.The experimental design entails employing a five-fold cross-validation approach. This methodology involves dividing the dataset into five subsets, and for each iteration, four subsets are used as the training set, while the remaining subset serves as the test set. This process is repeated five times, ensuring that each subset is utilized as the test set once, thereby facilitating robust evaluation and validation of the model’s performance. Cross-validation is valuable when tuning hyperparameters of the model. It allows us to iteratively evaluate the model’s performance with different hyperparameter settings and choose the best combination that generalizes well across various subsets of the data. data distributions may change over time due to various factors (data drift). Cross-validation can help identify how well the model generalizes across different data distributions, providing insights into the model’s robustness.In the part of user emotion recognition, the listening records of 24,537 users for nearly a week since October 6, 2021 were crawled from “Netease cloud music”, including user ID, music ID and times of listening, which is the corresponding weight.F1 is used to measure the accuracy of the model in the multi-classification task, and the value range is 0-1. Before calculating F1, it is necessary to map musical emotion to the emotion class with the maximum corresponding value from the musical emotion distribution in the music-emotion matrix V, and then calculate. The higher the F1 value is, the better the training effect is. In this experiment, each group of tests were carried out three times, and the average value was taken as the results for discussion. The calculation of F1 is expressed as F value of the algorithm on each emotion class. Adding emotional tags had a significant impact on the emotion of “Excitement”, and its F1 increased by 47.62%.“Nostalgia” is generally the best emotional class, while the “quiet” is generally poor, which may be due to the fact that compared with other emotional categories, the performance of “Nostalgia” is generally the best, Listening groups generally have similar cognition on whether music is “Nostalgia”, while “Quiet” and “Cure”, “relaxing” and other emotional categories overlap, which is not easy to distinguish.In order to further verify the effectiveness of the matrix decomposition method in the music domain data set generated by users, the method in this article is compared with the other five methods: K”-means, support vector machine (SVM), logistic regression (LR), AdaBoost, decision tree (DT). The results are shown in  The comprehensive analysis of the experimental results reveals intriguing insights into the performance of various models. The non-negative matrix factorization (NMF) technique, when employed with emotional labels, showcases the best overall performance, surpassing AdaBoost, the second-ranking model, by a remarkable margin of approximately 12.5%. This noteworthy difference indicates the superiority of nonnegative matrix factorization (NMF) in capturing underlying patterns and relationships among the emotional features, leading to more accurate classification results.Notably, k-means exhibits comparable outcomes to logistic regression (LR). The k-means algorithm partitions data into clusters, effectively separating instances with similar features. This behavior aligns with LR’s capacity to find decision boundaries in a linear manner, hence yielding similar results. However, NMF provides an added advantage by decomposing the original matrix into two non-negative matrices that approximately approximate the initial data, thereby mapping features into a hidden space simultaneously. This dual mapping contributes to enhanced classification efficacy.On the other hand, the decision tree model’s performance falls short, displaying inferior results compared to other models. Decision trees are prone to overfitting and may struggle to generalize well on complex datasets. Consequently, this negatively impacts the model’s recognition effectiveness.Overall, while some models perform similarly, it is evident that NMF with emotional labels significantly outperforms the rest. Its ability to extract meaningful representations from the data, combined with a dual mapping approach, highlights its potential for various applications, such as sentiment analysis, emotion recognition in text or speech, and user behavior prediction. Further research could explore parameter tuning and feature engineering to optimize the models further, ultimately advancing the field of emotion-based machine learning and data analysis.In order to better identify users’ emotions, it is necessary to classify music accurately. The algorithm in  More than half of the music that should have belonged to the class of “Relaxation” was wrongly assigned to the category of “Cure” and “Nostalgia”, while less was wrongly assigned to the category of “Lonely”. This phenomenon is consistent with cognition. The two class of “Relaxation” and “healing” do overlap. The concert of “Cure” brings people the feeling of “Relaxation”. Similarly, relaxing music can also make people feel “Cure”. Most (40.67%) of the music that should have belonged to the class “Quiet” were wrongly assigned to the class “Cure”, followed by the class “Nostalgia”. Combined with the practical experience in daily life, most of the music under the “Quiet” is pure music, or relatively soft and soothing music, and these music may also have the function of “Cure”, which can regulate people’s mood and emotion and make people calm down. Therefore, this phenomenon is reasonable and conforms to common sense cognition.To sum up, due to the limitation of data, the reason for this phenomenon is that the limitation of F1. Music contains only one emotion tag, that is, the emotion class with the highest value, while the emotion of music does not necessarily belong to a single class. The above results are consistent with the actual situation, so the reliability of the algorithm in this article is verified.When identifying user emotions, the number of nearest neighbor music k of unknown music will affect the result of music emotion recognition, and then affect the result of user emotion recognition. Therefore, parameter experiments need to be carried out around k. k = 27, with a remarkable score of 0.509. This suggests that the parameter k profoundly impacts the overall effectiveness of the system. Among the 119 users, a substantial portion of 59 users achieved accurate emotion recognition, resulting in an impressive accuracy rate of 52.7%. This finding underscores the system’s reliability in detecting emotions, positively impacting user experiences and applications in various fields such as sentiment analysis, user feedback processing, and emotional well-being assessments. Further studies could explore optimal k-values and refine the system for even better performance. The result of user emotion recognition is shown in The analysis of F value is 0.4286; The highest F value in the “Cured” category was 0.2069, the highest F value in the “Relaxed” category was 0.125 and the highest F value in the “Quiet” category was 0.25. Similar to the previous content, it shows that this is due to the similarity of the three classes, high degree of coincidence and close emotion. For example, “Cure” and “Quiet” music can also make people feel relaxed. Most relaxing music is gentle and soothing, so it is difficult to distinguish them. The other four categories have relatively obvious characteristics, such as “Excitement” music usually has a faster rhythm, while “Lonely” music usually have slow rhythm and sad words. In short, the overall performance of the algorithm in this article is good, which can effectively distinguish emotions under seven emotion categories and identify users’ emotions.The algorithm performs well in the four classes of “Romance”, “Nostalgia”, “Excitement” and “Lonely”. The algorithm performs best in the class of “Lonely”, with the optimal F value up to 0.6415, the highest F value in the category of “Nostalgia” is 0.6071, and the highest F value in the category of “Excitement” is 0.5641. In the “Romantic” category, Emotional analysis is divided into three stages: emotional expression stage, emotional perception stage and emotional induction stage. The emotional communication of users in the process of enjoying music can also be divided into three stages: firstly, the emotion of music is expressed through audio and lyrics, which is the emotion that the performer or composer wants to express; then, when the user hears the music, the emotion in music is perceived by users; finally, the user’s own emotion is aroused, that is, the user’s emotional response when listening to music. The user’s emotional response is not necessarily consistent with the perceived emotion, which may be related to the user’s own experience and mood at that time.This article aims to study the emotion of users when they hear music, and a set of algorithm is designed to identify user emotion, which has important practical application value. The specific application scenario is shown in (1) Music platform:The optimization of recommendations on the music platform can be achieved through the discernment of user emotions, as emotion represents a pivotal dimension in the recommendation process. By classifying music based on emotions, it becomes feasible to accurately capture users’ inclinations and preferences for music imbued with varying emotional content, thereby refining the recommendation system. Furthermore, the algorithm introduced in this article facilitates swift computation of user emotions, empowering the platform to monitor users’ emotional states in real-time and grasp the latest emotional trends. By discerning users’ moods, the platform can execute more intelligent marketing strategies. For instance, if a user’s emotional level appears negative for an extended period, the platform can recommend content like relaxing books or travel packages to uplift their spirits.(2) Depression treatment:Additionally, the findings of this research hold practical significance in the domains of user psychology and social psychology. Psychological studies have substantiated that individuals afflicted with depression often gravitate towards low-energy and melancholic music due to the soothing impact of such compositions . For depMoreover, matrix decomposition can easily integrate data from multiple modalities, such as combining audio features with lyrics or user-specific contextual information. This integration can lead to a richer and more comprehensive representation of emotions. The matrix decomposition approach can be adapted to capture individual differences in emotion perception. By learning personalized representations for users, the algorithm can enhance emotion recognition accuracy and provide more tailored music recommendations.In this article, starting from the user’s behavior of creating a song list and assigning a label to the song list, an NMF algorithm integrating emotional tags is proposed. Based on the massive social media data generated by users, the emotion of music is represented. The results show that this method can effectively reduce the cost of manual annotation, and the integration of external information can effectively improve the effect of music emotion recognition. In addition, a music emotion recognition algorithm is designed, which can directly obtain the corresponding emotion of music, or obtain the emotional representation of the music through similarity weighted calculation. Experiments show that the algorithm in this article performs well on the whole, and can effectively distinguish emotions under seven emotion categories and identify users’ emotions, which can effectively identify the emotion contained in massive unknown music. In specific application scenarios, the rational use of the model is expected to help users with depression get rid of depression in time.10.7717/peerj-cs.1590/supp-1Supplemental Information 1Click here for additional data file.


The global pandemic has severely disrupted the performing arts sector, with research documenting economic, professional, and health impacts on musicians. The psychological effects of lockdowns have been recognized, but little is known regarding their impact on freelance creative collaborative artists. This qualitative case study uses a resilience lens to report the perspectives of freelance creative collaborative musicians from the city of Melbourne, the Australian city which experienced the greatest period of lockdown in the country. Three main themes were identified: professional impacts , personal impacts , and future professional outlook . The findings demonstrate these musicians’ resilience in spite of difficult circumstances, resulting in positive adaptations and personal growth. Since the start of the worldwide Covid-19 pandemic in early 2020, performing artists have been significantly affected by public policies of social distancing and community lockdowns leading to cancellations of concerts, live performances, theater, dance, and musicals. This in turn has produced a body of research publications dating from 2021, investigating the impact of the pandemic on the professional and personal lives of performers.the pandemic and the entrenched inequalities it has revealed has demonstrated that our experiences of the pandemic were and are hugely diverse, determined by financial and physical security or precarity, the nature of our work, whether we had children or others to be cared for. , p. 13Even before the pandemic, performing arts sector careers were precarious. A longitudinal case study of eight freelance brass musicians in the United Kingdom showed that a portfolio career in the 21st century involves “concurrent multiple job roles” and, in addition to musical expertise, requires “other softer skills such as the ability to network both formally and informally, be entrepreneurial as well as use forms of social media as a method of modern-day promotion” , p. 351.Creativity in Crisis, describing losses of 80,000 jobs and $24 billion in income  requires resilience. The theory of resilience has been a focus of research since the early 1990s, and its conceptualization has changed over time. For example, Factors contributing to personal resilience include “self-confidence and high levels of self-efficacy” , p. 149.influences how an event is appraised, whereas coping refers to the strategies employed following the appraisal of a stressful encounter” . Terms such as resilience and coping are frequently used in a similar way; however, conceptual distinction between the two needs to be clarified. Little is known regarding resilience and coping strategies of professional musicians, despite much research into their generally poor physical and mental health e.g., , music pA Melbourne-based contemporary music ensemble was the case for this research . The groThe usual approach to their ensemble work had been severely disrupted during Melbourne lockdowns, forcing the group to pivot to new ways of creating and collaborating. These included undertaking initial planning meetings via Zoom, and creating and recording individual contributions to the collaboration over the period of a single day while working in isolation, with contributions being subsequently edited into a final work. This article reports on two creative collaborative projects.Ethical clearances were obtained from an Australian university prior to the commencement of research. A qualitative approach was chosen to obtain rich data and multiple points of view from the participants. Individual interviews provided an opportunity to probe deeply, ask for clarifications, and confirm understandings. Interviews were audio recorded and transcribed by a professional transcription company. Member-checking of interviews provided further opportunity for participants to reflect on their perspectives and the data provided.Members of the group gave their informed consent to be interviewed twice during April to September 2021, regarding two different projects. Six participants were interviewed regarding the first project and six regarding the second, with three core members being the same (interviewed on both occasions) and three project contributors different between the two projects . Due to the bespoke nature of the ensemble, specific information regarding age, sex, and ethnicity has been withheld to protect the participants. The participants ranged from mid- to late-career, and some had caregiving responsibilities for children. Only two reported receiving temporary state-funded support through JobKeeper.Semi-structured interviews probed individual music career histories as a creative collaborative musician, personal and professional Covid-related impact, lockdown adaptations in creative practice, and future outlook. This article focuses on the impacts of recurring lockdowns on the personal and professional lives of these freelance creative collaborative musicians.The interviews were initially coded by one researcher under three main themes: personal impacts of lockdowns, professional impacts, and future outlook . The emeWhen discussing the professional impact of recurring lockdowns, the participants highlighted three major issues: loss of work, loss of artistic identity, and professional coping strategies that sustained them during pandemic.The participants described the first few weeks of pandemic as endless emails canceling work, trips, and recording studio sessions, with their professional lives “crumbling down.” A nationally significant art gallery that supported 2,500 artists closed after 14 years of operation. As one respondent reported, “The first thing that happened is that a lot of my work just disappeared. My wife’s work disappeared completely, she’s a singer and all of her work is live performance.” Tours that took years of planning were canceled, leaving musicians in limbo: “Not being able to leave the country or the state has changed a huge amount of my life.”I was in a quarantine and couldn’t leave my house for two weeks. And then we went immediately into rehearsals for the project, and then our project got shut down due to the next lockdown about 10 days later.Being a freelance musician during the pandemic was particularly “daunting” with the financial impact of no regular income. In the state of Victoria, there were blanket bans on face-to-face work; for example, “the Rising Festival was cancelled the day after opening night.” One participant described it as a “roller coaster time”:The sudden interruption of normal working routines resulted in uncertainty about the future and made musicians feel like they were “artists in theory and not in practice.” One participant reported that “it felt very artificial to be working on projects that had no obvious future . . . it suddenly became a little meaningless to be making work when I couldn’t be making it with other people.” Another musician had a complete reassessment of their career: “It definitely made me re-evaluate everything, my future, my practice, where the opportunities are going to lie and perhaps where they won’t lie.”The respondents described the cumulative effects of lockdowns on the arts community as “apathy” and “creative doldrums.” One person reflected, “When I looked back and particularly in the second lockdown, I realized what a dark place I’ve gone to. I had stopped doing a lot of the things that I usually love.”The participants found the loss of professional connections “very disorientating.” One musician explained, “Going and seeing work and attending festivals and having that social network, not online but physically, is a really important part of how you orient yourself in your career. That had just disappeared, basically.”If I could record five minutes of music, or edit five minutes of music, answer some emails, and have a phone call, it was still a goal and it was important to be satisfied with the fact that I could do something.To sustain themselves during recurring lockdowns, the participants adopted a range of professional coping strategies. Focusing on “small wins” while working from home on new projects was one approach, as reported by a participant:I have a 30-minute to an hour-long routine that I do if I don’t have anything else currently to practise. I think I wanted to make sure that I didn’t come out the other side not able to play because of the lockdown.Maintaining instrumental practice and mastering other instruments was seen as an opportunity to stay fit and extend current performance skills:Developing new ideas for future projects helped the participants remain creative. For example, one person used the 1-hr of permitted exercise to brainstorm ideas for future projects with others: “I’ve been meeting up with a range of different artists, walking down the river or walking through the neighbourhood and sometimes just catching up, other times planning projects, and just digesting ideas.” Others viewed the lockdowns as an opportunity to develop their own work: “It’s given me time to be able to focus on some bigger ideas with greater generosity.”Overall, the findings demonstrate a pattern of loss followed by some recovery characterized by specific coping strategies. The findings showed that professional impacts experienced by Australian freelance creative collaborative musicians were similar to other musicians around the world, with cancelations of performances and tours resulting in loss of work and income . The losresilience in spite of difficult circumstances. The perspectives of creative collaborative musicians regarding the impacts of the Covid-19 lockdowns on their professional lives reported here demonstrate their Initially, cancellations of performances had resulted in a loss of artistic identity for the musicians in this study. Their appraisal of the drastic pandemic situation over time led to professional coping strategies as well as long-term positive adaptations in the form of exploration and expansive practices. As Participant reflections on personal impacts of recurring lockdowns focused on three areas: lockdown stressors, personal coping strategies, and relationships.Lockdown stressors were front-and-center when the participants reflected on the personal impacts of the pandemic. Having observed friends and colleagues battling Covid-19, the participants were afraid of the disease and recognized its severity: “It made me quite fearful of getting it. And it made me quite angry at people who weren’t taking lockdowns and safety measures seriously. It made me very wary about big groups.”The thing that is missing in my life is adrenaline. And that used to be a huge part of the chemical makeup of a performer, the extreme pressure of building up towards a performance that includes late nights, physical exertion, nerves, adrenaline, and then the huge sense of achievement. When those things are missing, it’s really hard, it’s flatlined and somewhat boring.Respondents commented on feeling worn out and flat:The lockdowns affected some musicians with self-doubts, as one reflection shows: “It’s definitely affecting people mentally, and it’s limiting their belief in themselves and their ability to share their music.” Others recognized the diversity of responses: “Everybody is living in completely different environments, not everyone has got the same coping strategies. We’re in the same storm, but everyone’s got completely different boats that they’re riding the storm in.”Some participants’ personal coping strategies “centred around food and cooking.” Exercise and hobbies served the purpose of filling in time during lockdowns and also giving a sense of achievement and control that was missing from everyone’s lives at the time. For example, one participant reported, “In the first lockdown this time last year, we were doing lots of hour-walks or two-hour walks with the family around the neighbourhood looking for flowers, wild or otherwise, cultivated or grown.” Another participant preferred indoor activities, “I did a lot of jigsaws, colouring in, played a lot of cards and I’ve found time to read for myself again.”Helping others in more difficult circumstances gave meaning and purpose for one participant: “I was doing some of that emotional labour heavy-lifting and checking in with friends and family and making sure that people are OK because I know I was not struggling as much as other people were.”On Monday nights, we used to have pasta and listen to this podcast. Tuesday night my partner used to play Dungeons and Dragons on the internet with his brother and their friends. Wednesday night used to be wine and cheese night. Friday night would always be delivery and fizzy wine night. Sunday nights became black-and-white movie nights.Planned recreational activities served as “markers in time.” One participant described regular themed nights:I felt that a conversation with a friend, a bit of a heart-to-heart, just hearing how they were going and how are you coping and what are you doing was also an important, a legitimate part of the day and that it was OK to see that as a successful day if you’d had a conversation like that, that it wasn’t just wasting time.I was happy to not go and see my dad and give him a fatal disease, I could only talk to him on Zoom. I was missing seeing him but it was so obviously the better thing for him.Maintaining relationships with friends and family online or in person played an important role in supporting the participants emotionally during lockdowns, as seen in the quotations below:The findings show that the main personal impacts raised by the participants were fear of illness, exhaustion, and doubts regarding careers. Freelance cultural workers in the United Kingdom similarly reported “increased psychological and physical symptoms as a result of their changing working lives and the consequences of these changes on their professional identities” , p. 16. The participants discussed personal coping strategies of filling the time at home during lockdowns with purposeful activities such as cooking, exercise, hobbies, helping others, and planned recreation. This finding aligns with previous research where a synthesis of 16 studies demonstrated that “exercise, yoga, progressive muscle relaxation, and listening to relaxing music” had beneficial effects in promoting well-being during lockdowns , p. 2. CFocus on relationships through online social interactions with family and friends was also important for the participants. Similarly, The participants described processes of adaptation to lockdown stressors  and demonstrated resilience on a personal level. These included being proactive through purposeful activities for self-regulation and sense of control, helping others and maintaining close relationships for emotional support. Research highlighted such resources as factors in resilience building and maintenance .The participants’ future outlook focused on three issues: developing new professional skills and future directions, positive future outlook, and negative future outlook.The participants stated that the pandemic forced them to learn new technological skills such as “communicating online or having meetings online or teaching online.” New music-making opportunities necessitated development of new skills: “Livestreaming mix sessions was an innovation that was crucial to collaboration in that time. It made me hone in a little bit more on technical aspects of my studio.”When planning for future projects, respondents tried to think laterally and “do something that hasn’t existed before.” As one participant reflected, “I’m putting a lot more of my energy into the planning stages at the moment, into fostering those relationships, and into looking at how we maybe find some projects and into dreaming up what might be next.”My focus this year has shifted from performance to more of admin and more about how we emerge from this time ready to do great projects and to talk about our work. I was very lucky earlier this year to get some funding towards mentoring, so I’ve been spending a lot of time with four different mentors lately.In the absence of performance opportunities, greater effort went into administration and grant-writing:I developed an online program that we ran for high school students in the September school holiday. So specifically for students who were still in the lockdown and who basically spent a week with us getting creative prompts from a range of different performers and composers.Maintaining engagement within the community and creating new opportunities was viewed as essential and productive: “Being in one place for a significant amount of time, not leaving, re-engaging with community, especially with a community undergoing significant hardship has led to new friendships, and new routines, and new collaborative outcomes with various different people.” For example, one participant reported:The pandemic is going to take a little bit of the appetite out of just subsidizing opera and symphony orchestras to high degrees outside of the pandemic, which I think is a great opportunity for a diversified sector to come through. I do think more support and more opportunity for the small-to-medium sector would create a much more interesting musical landscape in Australia.The participants with a positive future outlook discussed the need for industry innovation, “to dig deeper, become more engaging, more relevant to contemporary society, more viable in a variety of ways.” For example, they highlighted the potential for greater support for the small-to-medium arts sector versus the bulk of the funding being channeled into large national companies:You need to be in a room and you need to feel that response from that other person, you need to be in those free-form conversations where you’re just like, all cylinders firing. And I think artists can’t wait to get back in there.The respondents commented on the public’s appetite for the return to live performances: “I can’t wait to be able to go to a live gig, to go to the theatre, even to wander around an art gallery and have some toddler crash into me as they are screaming and yelling.” Face-to-face live interactions were seen as vital for creative work:Screen fatigue was cited as a factor: “Everyone’s pretty sick of being on their computer and being on Zoom.” As one participant explained, “People went digital because they had to, but I don’t think they will end up being a substitute for the live performance.”It’s difficult to see concrete ways forward as an independent artist, but the real sadness is seeing the sector lose talent now. There are a lot of people who are choosing to leave the sector. I feel a little more cautious, and maybe a little sadder about the state of the industry and where some of my colleagues are at.Some older participants exhibited a negative future outlook, displaying symptoms of the “great resignation”: “I’m just really demoralized, and I’m just really, really tired. I’m re-evaluating my career choices right now, I’m exhausted and I kind of feel like I just need out.” Others were concerned about the loss of cultural life in the future:Planning future projects and developing new technological and administrative skills to support these new ideas led to a positive future professional outlook displayed by many of the participants in this study. Similar findings have been reported by North American research flagged the need for innovation in the industry, concluding that to survive post-Covid recession “symphony orchestras will need to practice cost-cutting measures, while reinventing themselves through innovative programmes, including increased streaming platforms” , p. 1. Sde facto, have retired” (p. 12). Similarly, In contrast, The participants demonstrated many interpersonal resources that contributed to building and maintaining resilience: personal growth in acquiring new technological skills and innovative approaches to future projects, problem-solving skills to generate new ideas for future projects, and coping strategies to regulate emotions and sustain optimism and hope. These characteristics have been highlighted by research as necessary elements of resilience .The resilience demonstrated by the creative collaborative musicians in this study stands in contrast to the lack of support provided by the Australian Federal government to small arts organizations during the pandemic. This suggests that while resilience may be a positive skill for musicians, in this instance, it is something that is enforced by the economic and cultural conditions in the sector . Policy This study documents resilience among a group of creative collaborative musicians during recurring lockdowns, and the findings are necessarily limited to the specific case. In spite of lockdown stressors , the ensemble managed to create and disseminate new work. The capacity to bounce back from loss and to even expand their collaborative practices was perhaps only possible owing to a number of resilience resources, including a well-established collaborative network, strong interpersonal relationships, creative skills, and coping strategies. It is also important to note that positive adaptation, labeled as “resilience,” may in fact be masking vulnerability and that the far-reaching effects of the Covid-19 pandemic remain, at the time of writing, largely unknown. As The findings represent perspectives of members of a specific group of music creatives who have worked together for a number of years. The ensemble’s modus operandi of creating new work with new collaborators over one day and presenting live to an audience on the same evening has prepared the participants to work under pressure, be flexible and open to new ideas, deal with the unexpected, and trust each other. Furthermore, a sustained history of working in the small-medium arts sector, where generating one’s own creative projects is the prime focus, may well have contributed to this group’s more positive reports.awareness of self and musical identitie(s), ability to  handle concurrent multiple job roles, hard and soft skill sets which can adapt to a range of environments and demands, as well as a clear baseline of expert knowledge which can be adapted to specific contexts. , p. 351The strong professional and personal relationships have allowed the ensemble to pivot in new directions and withstand the drastic impacts of lockdowns such as social isolation and restriction on movement. As research has pointed out, a sustainable career in music requiresPrevious research has focused on freelancing musicians working in larger organizations such as orchestras e.g., , 2022. TFor us as higher education music educators and researchers, concerns about the impacts of lockdowns on current undergraduate music students and recent graduates starting their careers remain the focus for the profession if we are to sustain the artistic practice and cultural lives of contemporary society. As


Informed by our lived experiences with eating disorders, our work providing direct support to communities underserved by existing healthcare structures, and our commitment to social justice, we are deeply troubled by several aspects of the proposed characteristics for “terminal” anorexia nervosa outlined by Gaudiani et al. in Journal of Eating Disorders . We have identified two substantial areas of concern in the proposed characteristics provided by Gaudiani et al. and the subsequent publication by Yager et al. . First, the original article and the subsequent publication fail to adequately address the widespread inaccessibility of eating disorder treatment, the lack of parameters for what constitutes “high quality care”, and the prevalence of trauma experienced in treatment settings for those who do access treatment. Second, the characteristics proposed for “terminal” anorexia nervosa are constructed largely based on subjective and inconsistent valuations of suffering which build on and contribute to harmful and inaccurate eating disorder stereotypes. Overall, we believe these proposed characteristics in their current form stand to detract from, rather than assist, the ability of patients and providers to make informed, compassionate, and patient-centered decisions about safety and autonomy both for individuals with enduring eating disorders and for individuals with more recently diagnosed eating disorders. Gaudiani et al.  outlinedOur first concern is with characteristic (3), which necessitates “prior persistent engagement in high quality, multidisciplinary eating disorder care” , p. 11. Substantial and consequential differences in systems of ED care exist between countries, and a full comparison of their quality and accessibility is beyond the scope of this response. We will therefore focus on the context of the US, where the authors are based and where the three cases in the original manuscript  receivedTraumatic, coercive, and ill-suited experiences while in ED treatment are common , 18–21 aElwyn  additionAlthough the full relationship between systemic oppression, ED etiology, and healthcare inequity is beyond the scope of our correspondence, we will provide a brief description of barriers that impede marginalized and under-resourced people from accessing ED treatment that remain unaddressed by Gaudiani et al.  and YageAccess to care is also impacted by other identities and marginalization by systems of power. For example, ED diagnosis is typically delayed among people of color , 30 and Eating disorders also significantly co-occur with other psychiatric disorders, as seen in the case reports , which aAs outlined above, there are multiple and intersecting forms of systemic oppression and lack of competencies that may prevent access to safe, affirming, and collaborative eating disorder care. Due to these factors, some individuals with a long standing ED may have never received specialized or intensive ED care or have received care which may, on paper, match the course of treatment described by Yager et al. as optimal but which may have done personal harm and contributed to the maintenance of the ED. Iatrogenic harm in the context of ED care can produce a vicious cycle where prior traumatic treatment may preclude future willingness to engage in treatment, making outpatient clinicians reluctant or unwilling to work with individuals who they deem too high risk . In turnOur second concern is that the authors , 5 are uWhile the authors defend characteristic 1) (a diagnosis of AN) and (2) (age 30 or older) based on evidence of increased mortality rates in individuals with long standing AN age 30 or older in the literature [ (a diagnIn addition to the lack of clarity around defining SE-AN, multiple other factors may complicate staging criteria for AN. Given that deaths from medical complications of AN may be abrupt and unpredictable , some inDespite the known overlap between ED categories and the heterogeneity within enduring AN, in a podcast interview, Gaudiani argued that while death is possible from EDs other than low weight AN, it cannot be considered inevitable within 6 months of ceasing treatment , 87. GauThe authors repeatedly state that the proposed characteristics and the possibility of MAID apply only to a small minority of individuals with long standing AN. However, we believe that Gaudiani et al. proposed characteristics and the subsequent comment by Yager et al.  have andThe publications by Gaudiani et al. and Yager et al. also reinforce the mis-conceptualization of individuals with long-term, low weight AN as not only medically but also constitutionally distinct from other individuals with EDs. Gaudiani et al. repeatedly described the case presentations as “rare,” “select,'” “highly sensitive,” “brilliant,” “brave,” and “incisive” and portrays the individual with “terminal” AN as possessing access to family support, quality treatment options, and consistent expert care. Collectively, the choice of these adjectives and these case studies furthers the deleterious and inaccurate stereotype of EDs as illnesses that affect sensitive, perfectionistic, and highly intelligent individuals of considerable privilege and relatively few economic or identity-based barriers to ED care.The authors emphasize that both the case subjects and other hypothetical individuals meeting the proposed characteristics for “terminal” AN experience prolonged suffering, trauma from previous attempts at treatment, preference to avoid institutionalization with other ill individuals, extreme intolerance of weight gain, strong desire to have autonomy over their health care decisions, thoughts that future treatment is hopeless, and acceptance of inevitable death from their ED, which therefore precludes recovery oriented interventions , 5. ThesGiven the high rates of suicidality in EDs , 94, 95,Finally, while the authors state that the proposed characteristics for terminal AN should not impact the vast majority of individuals with EDs , the proGaudiani et al.'s framework for “terminal” AN assumes personal privilege, discounts systemic oppression, and fails to consider the interaction between diagnoses and perceived hierarchies across the full spectrum of EDs. The authors’ emphasis that terminal AN applies to “a select, rare group of adults” , p. 2 in