Physicians’ views of patient–planetary health co-benefit prescribing: a mixed methods systematic review
Bibliographic record
Abstract
Health professionals are increasingly called to become partners in planetary health. Using patient-planetary health (P-PH) co-benefit prescribing framing, we did a mixed methods systematic review to identify barriers and facilitators to adopting P-PH co-benefit prescribing by physicians and mapped these onto the Capability, Opportunity, Motivation, and Behaviour (COM-B) model and Theoretical Domains Framework (TDF). We searched electronic databases from inception until October, 2022, and did a content analysis of the included articles (n=12). Relevant categories were matched to items in the COM-B model and TDF. Nine barriers and eight facilitators were identified. Barriers included an absence of, or little, knowledge of how to change practice and time to implement change; facilitators included having policy statements and guidelines from respected associations. More diverse study designs that include health professionals, patients, and health-care system stakeholders are needed to ensure a more holistic understanding of the individual, system, and policy levers involved in implementing clinical work informed by planetary 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 | 0.039 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".