Canadian Experts' Views on the Importance of Attributes within Professional and Community-Oriented Primary Healthcare Models
Bibliographic record
Abstract
PURPOSE: The aim of this study was to rate the importance of primary healthcare (PHC) attributes in evaluations of PHC organizational models in Canada. METHODS: Using the Delphi process, we conducted a consensus consultation with 20 persons recognized by peers as Canadian PHC experts, who rated the importance of PHC attributes within professional and community-oriented models of PHC. RESULTS: ATTRIBUTES RATED AS ESSENTIAL TO ALL MODELS WERE DESIGNATED CORE ATTRIBUTES: first-contact accessibility, comprehensiveness of services, relational continuity, coordination (management) continuity, interpersonal communication, technical quality of clinical care and clinical information management. Overall, while all were important, non-core attributes - except efficiency/productivity - were rated as more important in community-oriented than in professional models. Attributes rated as essential for community-oriented models were equity, client/community participation, population orientation, cultural sensitivity and multidisciplinary teams. CONCLUSION: Evaluation tools should address core attributes and be customized in accordance with the specific organizational models being evaluated to guide health reforms.
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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 | 0.047 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".