The rationales for and challenges with employing arts-based health services research (ABHSR): a qualitative systematic review of primary studies
Bibliographic record
Abstract
Health services research (HSR) is an interdisciplinary field that investigates and improves the design and delivery of health services from individual, group, organisational and system perspectives. HSR examines complex problems within health systems. Qualitative research plays an important role in aiding us to develop a nuanced understanding of patients, family, healthcare providers, teams and systems. However, the overwhelming majority of HSR publications using qualitative research use traditional methods such as focus groups and interviews. Arts-based research-artistic and creative forms of data collection such as dance, drama and photovoice-have had limited uptake in HSR due to the lack of clarity in the methods, their rationales and potential impacts. To address this uncertainty, we conducted a qualitative systematic review of studies that have employed arts-based research in HSR topics. We searched four databases for peer-reviewed, primary HSR studies. Using conventional content analysis, we analysed the rationales for using arts-based approaches in 42 primary qualitative studies. We found four rationales for using arts-based approaches for HSR: (1) Capture aspects of a topic that may be overlooked, ignored or not conceptualised by other methods (ie, quantitative and interview-based qualitative methods). (2) Allow participants to reflect on their own experiences. (3) Generate valuable community knowledge to inform intervention design and delivery. (4) Formulate research projects that are more participatory in nature. This review provides health services researchers with the tools, reasons, rationales and justifications for using arts-based methods. We conclude this review by discussing the practicalities of making arts-based approaches commensurable to HSR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.547 | 0.699 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.050 | 0.038 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.010 | 0.015 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".