From Policy to Practice: A Qualitative Study on Reforms and Frontline Retention in Healthcare
Bibliographic record
Abstract
In Canada, healthcare reforms typically aim to improve the quality of care and access while making healthcare systems more efficient. These reforms have led to a 2-level healthcare system consisting of provincial and regional health authorities (RHAs). RHAs are responsible for providing and administering health services within specific territories. One of the 2 language-based RHAs in New Brunswick (NB) operates in French-speaking rural minority communities. This study explored key factors affecting the retention of nurses and physicians within a RHA operating in a language minority context. This descriptive qualitative study explored how macro-level decisions are experienced on the frontlines. Data were collected through semi-structured interviews with 21 physicians and 37 registered nurses, as well as 2 focus groups involving 20 key informants in managerial roles. Thematic analysis was used to identify key themes. Three main factors emerged: organizational accountability and frustration, local autonomy and contextual responsiveness, and a culture of openness and perceived loss of control. These factors are associated with policy changes that affect operational settings and resource distribution within the RHA and influence the retention of nurses and physicians. Stakeholders in health system reforms, including governments and RHAs, must recognize that policy adjustments can have direct implications on everyday care. Participants expressed a growing disconnect from decision-making hierarchies and a perceived loss of control. Both are seen as barriers to delivering quality care. Ensuring adequate support and resources for implementing system-level changes is key to fostering professional engagement and enhancing job satisfaction.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".