Explaining the Success or Failure of Quality Improvement Initiatives in Long-Term Care Organizations From a Dynamic Perspective
Bibliographic record
Abstract
The purpose of this study was to better understand why change initiatives succeed or fail in long-term care organizations. Four case studies from Québec, Canada were contrasted retrospectively. A constipation and restraints program succeeded, while an incontinence and falls program failed. Successful programs were distinguished by the use of a change strategy that combined "let-it happen," "help-it happen," and "make-it happen" interventions to create senses of urgency, solidarity, intensity, and accumulation. These four active ingredients of the successful change strategies propelled their respective change processes forward to completion. This paper provides concrete examples of successful and unsuccessful combinations of "let-it happen," "help-it happen," and "make-it happen" change management interventions. Change managers (CM) can draw upon these examples to best tailor and energize change management strategies in their own organizations.
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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 | 0.021 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".