Is it premature to formulate recommendations for policy and practice, based on culture and health research? A robust critique of the CultureForHealth (2022) report
Bibliographic record
Abstract
Introduction: Arts and health practice and research has expanded rapidly since the turn of the millennium. A World Health Organization scoping review of a large body of evidence claims positive health benefits from arts participation and makes recommendations for policy and implementation of arts for health initiatives. A more recent scoping review (CultureForHealth) also claims that current evidence is sufficient to form recommendations for policy and practice. However, scoping reviews of arts and health research-without critical appraisal of included studies-do not provide a sound basis for recommendations on the wider implantation of healthcare interventions. Methods: We performed a detailed assessment of 18 Randomised Controlled Trials (RCTs) on arts-based interventions included in Section 1 of the CultureForHealth report using the Joanna Briggs Institute Critical Appraisal Tool for RCTs (2023). Results: The 18 RCTs included demonstrated considerable risks of bias regarding internal and statistical conclusion validity. Moreover, the trials are substantially heterogeneous with respect to settings, health-issues, interventions, and outcomes, which limits their external validity, reliability, and generalisability. Conclusions: The absence of a critical appraisal of studies included in the CultureForHealth report leads to an overinterpretation and overstatement of the health outcomes of arts-based interventions. As such, the CultureForHealth review is not a suitable foundation for policy recommendations, nor for formulating guidance on implementation of arts-based interventions for health.
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
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 | 0.580 | 0.829 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.025 | 0.032 |
| Open science | 0.017 | 0.023 |
| Research integrity | 0.038 | 0.057 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".