Development of Best Practice Guidelines for Primary Care to Support Patients Who Use Substances
Bibliographic record
Abstract
INTRODUCTION: People who use substances often mistrust the primary care system, impeding access. OBJECTIVES: To build on research clarifying how to improve patients' feelings of safety, through co-creating best practice guidelines with physicians and patient representatives. METHODS: After obtaining Research Ethics Board approval, this qualitative study engaged 22 participants including patients, physicians, and health system partners. We held a series of workshops, co-facilitated by patients and researchers, corresponding to 3 phases of the research: (1) establishment of cultural safety processes for participants during the workshops; (2) a facilitated, collaborative world café to develop guideline content; (3) validation of best practice guidelines. An implementation plan was developed and implemented. Finally, an external peer review was conducted by McGill University. RESULTS: Best practices guidelines were developed giving the patient perspective on how to enhance primary care, as follows: (1) become trauma informed; (2) consider your clinical environment; (3) build a network; (4) supply an array of resources; (5) co-create a long-term treatment plan; (6) help me to stay healthy; (7) ensure timely access to specialized medical and surgical care; (8) be an advocate; (9) ask for feedback; (10) follow up. Resources were developed and disseminated. CONCLUSION: The best practice guidelines reflect the patients' perspectives on common challenges patients have encountered, which impede their access to primary care. They support primary care physicians in providing more effective services to this challenging population of patients.
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 | 0.061 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".