An Exploration of the Methods of Communication between Policy Makers and Providers that Help Facilitate Implementation of Primary Health Care Reforms
Bibliographic record
Abstract
Policy reforms targeting organizational structure, expansion of interprofessional teams, inclusion of collaborative practices, and shifting provider remuneration models have resulted with substantial change for providers and leaders with primary health care settings in Canada, USA, and elsewhere. Discourse analysis provides a theoretical lens that can help build an understanding about the implications of different modes of communication on the implementation of new policy initiatives like new models of primary health care. This study applies discourse analysis to determine the modes of communication that were used to relay policy expectations underpinning a newly emerging interprofessional model of primary health care. We conducted a secondary analysis of a qualitative study conducted between 2010 and 2011 with primary health care leaders and policy informants during a period of health care reform in Canada. In-depth semi-structured interviews were conducted with seven key policy informants (PIs) and 29 primary health care leaders (physicians, executive directors, and non-physician clinical leaders). Discourse analysis is useful in the investigation of the meanings of health, health policy, and health care settings.
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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 | 0.065 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.017 | 0.026 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".