Bibliographic record
Abstract
BACKGROUND: Trust is integral to effective interprofessional collaboration. There has been scant literature characterizing how trust between practitioners is formed, maintained or lost. The objective of this study was to characterize the cognitive model of trust that exists between pharmacists and family physicians working in collaborative primary care settings. METHODS: Pharmacists and family physicians who work collaboratively in primary care were participants in this study. Family health teams were excluded from this study because of the distinct nature of these settings. Through a snowball convenience sampling method, a total of 11 pharmacists and 8 family physicians were recruited. A semistructured interview guide was used to guide discussion around trust, relationships and collaboration. Constant-comparative coding was used to identify themes emerging from these data. RESULTS: Pharmacists and family physicians demonstrate different cognitive models of trust in primary care collaboration. For pharmacists, trust appears to be conferred on physicians based on title, degree, status and positional authority. For family physicians, trust appears to be earned based on competency and performance. These differences may lead to interprofessional tension when expectations of reciprocal trust are not met. CONCLUSIONS: Further work in characterizing how trust is developed in interprofessional relationships is needed to support effective team formation and functioning.
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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 | 0.021 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".