Lessons on integrated knowledge translation through algorithm's utilization in homecare services: a multiple case study
Bibliographic record
Abstract
AIM: Integrated knowledge translation (IKT) is an increasingly recommended collaborative approach to minimize knowledge translation gap. Still, few studies have documented the impact of IKT to optimize knowledge uptake in healthcare settings. An IKT-based clinical algorithm (Algo) was deployed in Quebec (Canada) homecare services to support skill mix for selecting bathing equipment for community-dwelling adults. The objective of this study was to document the characteristics related to Algo's IKT process. METHODS: A multiple-case study with a nested concurrent mixed design was conducted in provincial homecare services. Based on Knott and Wildavsky's seven-stage classification and the integrated-Promoting Action on Research Implementation in Health Services model, Innovation, Recipients, and Context, characteristics related to Algo's levels of utilization were documented. Quantitative (electronic questionnaire) and qualitative (semistructured interviews and focus groups) data were collected for each case (i.e., homecare service). Descriptive statistics and thematic analysis were performed to describe each case through a mixed methods matrix, for intra/intercase analyses. RESULTS: Knowledge translation characteristics of five Algo's levels of utilization were documented: reception, cognition, reference, effort, and impact. Innovation characteristics (e.g., underlying knowledge) were found to facilitate its dissemination and its use. However, the Recipients (e.g., unclear mechanisms to implement change) and Context (e.g., organizational mandates nonaligned with skill mix) characteristics hampered its application through intermediate and advanced levels of utilization. CONCLUSION: The knowledge translation analysis of Algo allowed for documenting the IKT-based benefits in terms of utilization in healthcare settings. Although an IKT approach appears to be a strong facilitator for initiating the implementation process, additional characteristics should be considered for promoting and sustaining its use on local, organizational, and external levels of context. Facilitation strategies should document the administrative benefits related to Algo's utilization and contextualize it according to homecare services' characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".