Exploring the fit between organizational culture and quality improvement in a home-care environment
Bibliographic record
Abstract
BACKGROUND: Overseeing the quality of community-based, home-care services is a subject of concern in most jurisdictions confronted with population aging and the rise of chronic conditions. Although various quality management strategies have been used in different health care settings, continuous quality improvement (CQI) is still in the early stages of development among home-care service providers. What is more, some authors have raised questions as to whether CQI is suitable to the unique character of home-care and can be adequately applied to a diverse and varied range of agencies, each featuring a unique organizational culture, professional mix, and mode of operation. PURPOSES: The article reports on how differing organizational cultures--as found in a set of public and private home-care providers--appear to affect agency receptivity to CQI during program implementation. METHODOLOGY/APPROACH: The research methodology is characterized by a qualitative, multiple case study approach. Data were gathered from a purposive sample of four home-care agencies in Quebec, Canada, belonging to the public, private for-profit, and not-for-profit sectors. FINDINGS AND PRACTICE IMPLICATIONS: It is concluded that a core set of cultural attributes play a decisive role in determining agency receptivity to CQI, even when its effect is mediated by several contingent variables. Further, some of the levers and barriers to implementation identified in previous research seem less relevant to home-care agencies. A number of policy/management implications are discussed, which may enhance receptivity to CQI by home-care agencies and prevent implementation failure.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".