Identifying and De-implementing Low-value Administrative Practices in Long-term Care
Bibliographic record
Abstract
For continued survival, organizations must undergo changes to their structure, routines, and practices, which are facilitated by organizational learning and unlearning. The concept of unlearning is highly relevant in healthcare organizations, where information is continually updated and resources may be limited. In health services research, organizational change is often discussed in terms of implementation and de-implementation. As such, the objectives of this dissertation were: (i) to explore the conceptual and practical characteristics of organizational unlearning in the health services research context, and (ii) identify and characterize the subject of organizational unlearning with respect to administrative work in Canadian long-term care homes, and the processes that may contribute to their de-implementation. This was achieved through three complementary studies. The first study was a concept analysis on organizational unlearning. This concept analysis demonstrated that organizational unlearning involves five key attributes: plan, action, subject of organizational unlearning, value, source. Antecedents and consequences were described. The second study was a scoping review to understand the approaches used in the existing empirical literature to explore the phenomenon of unlearning within an organizational setting. This review offers four key findings: (1) De-implementation is the most common terminology used to describe organizational unlearning in health services research; (2) The most common methods used to study unlearning was quantitative chart reviews and qualitative interviews; (3) Educational strategies were the most common experimental strategies used to facilitate unlearning; and (4) there is inconsistency with use of theories, models, and frameworks, across studies. The third study employed qualitative interviews to explore de-implementation in a health care setting. Specifically, low-value administrative practices in the Canadian long-term care sector. Twenty-seven low-value administrative practices were identified and characterized. The details of each practice were presented as cases. In nine cases, de-implementation processes were described, including the stakeholders engaged, strategies employed, and outcomes achieved. The findings of this dissertation move beyond the important conceptual and theoretical work that has been done to facilitate de-implementation efforts in practice. As was demonstrated here, such efforts can be meaningfully applied to the long-term care, where resources with respect to time, money, and staff are increasingly limited.
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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 | 0.058 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 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".