Awareness and Use of Community Services among Primary Care Physicians
Bibliographic record
Abstract
Primary care physicians play an important role in care coordination, including initiating referrals to community resources.Yet, it is unclear how awareness and use of community resources vary between physicians practising with and without an extended healthcare team.We conducted a cross-sectional survey of primary care physicians practising in Toronto, Canada, to compare awareness and use of community services between physicians practising in team-and non-team-based practice models.Team-based models included Community Health Centres and Family Health Teams -settings in which the government provides funding for the practice to hire non-physician health professionals, such as social workers, pharmacists, nurse practitioners, registered nurses and others.The survey was mailed to physicians, and reminders were done by phone, fax and e-mail.We used logistic regression to compare awareness between physicians in team-based (N = 89) and non-team-based (N = 138) models after controlling for confounders.We found that fewer than half of the physicians were aware of five of eight centralized intake services (e.g., ConnexOntario, Telehomecare).For most services, team-based physicians had at least twice the odds of being aware of the service compared to non-team-based physicians.Our findings suggest that patients in team-based practices may be doubly advantaged, with access to non-physician health professionals within the practice as well as to physicians who are more aware of community resources. Résumé[60] HEALTHCARE POLICY Vol.16 No.1, 2020 service, comparativement aux médecins qui n' œuvrent pas au sein d' une équipe.Nos résultats suggèrent que les patients qui consultent dans les pratiques où se trouvent des équipes bénéficient d' un double avantage, d' une part grâce à l' accès aux professionnels de la santé non médecins, et d' autre part grâce aux médecins qui sont plus au fait des ressources communautaires disponibles.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".