Operational Definitions of Attributes of Primary Health Care: Consensus Among Canadian Experts
Bibliographic record
Abstract
PURPOSE: In 2004, we undertook a consultation with Canadian primary health care experts to define the attributes that should be evaluated in predominant and proposed models of primary health care in the Canadian context. METHOD: Twenty persons considered to be experts in primary health care or recommended by at least 2 peers responded to an electronic Delphi process. The expert group was balanced between clinicians (principally family physicians and nurses), academics, and decision makers from all regions in Canada. In 4 iterative rounds, participants were asked to propose and modify operational definitions. Each round incorporated the feedback from the previous round until consensus was achieved on most attributes, with a final consensus process in a face-to-face meeting with some of the experts. RESULTS: Operational definitions were developed and are proposed for 25 attributes; only 5 rate as specific to primary care. Consensus on some was achieved early (relational continuity, coordination-continuity, family-centeredness, advocacy, cultural sensitivity, clinical information management, and quality improvement process). The definitions of other attributes were refined over time to increase their precision and reduce overlap between concepts (accessibility, quality of care, interpersonal communication, community orientation, comprehensiveness, multidisciplinary team, responsiveness, integration). CONCLUSION: This description of primary care attributes in measurable terms provides an evaluation lexicon to assess initiatives to renew primary health care and serves as a guide for instrument selection.
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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 | 0.122 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".