Deimplementing Untested Practices in Homecare Services: A Preobservational-Postobservational Design
Bibliographic record
Abstract
INTRODUCTION: With community-dwelling elders waiting to adapt their bathroom, Health and Social Services Centers in Quebec (Canada) combined human resources through cross-skilling within interdisciplinary teams. To this end, occupational therapists implemented in-house "tools" to support nonoccupational therapists in selecting bathing equipment. However, unknown psychometric properties of those in-house "tools" cast doubt on the quality of service provided to elders. Little is also known about the best processes to use to support the deimplementation of such nonevidence-based practices. This study presents the effect of a knowledge transfer and exchange intervention designed to deimplement in-house "tools" and replace them with an evidence-based tool (Algo). METHODS: Censuses were conducted with the 94 Health and Social Services Centers of Quebec providing homecare services, before and after the knowledge transfer and exchange intervention (2009-2013). In 2013, the deimplementation of in-house "tools" and their replacement by Algo were measured with Knott and Wildavsky's levels of utilization. RESULTS: Cross-skilling within interdisciplinary teams increased between censuses (87% to 98%), as did use of in-house "tools" (67% to 81%). Algo's uptake started during the knowledge transfer and exchange process as 25 Health and Social Services Centers achieved the first level of utilization. Nonetheless, no Health and Social Services Center deimplemented the in-house "tools" to use Algo. CONCLUSION: The knowledge transfer and exchange process led to the development of a scientifically sound clinical tool (Algo) and challenged the status quo in clinical settings regarding the use of nonevidence-based practices. However, the deimplementation of in-use practices has not yet been observed. This study highlights the need to act proactively on the deimplementation and implementation processes.
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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 | 0.029 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".