Reflexive case studies on conducting technology implementation research in long-term care homes
Bibliographic record
Abstract
Background: Technological advancements offer the potential to address healthcare challenges, such as improving residents' quality of life in long-term care homes (LTCHs). However, there is often a mismatch between developed technologies and the actual needs of residents and staff, leading to poor adoption. Researchers conducting research on developing and implementing technologies in LTCHs face unique challenges. Understanding these challenges is crucial for enhancing technology adoption and sustainability in LTCHs. Methods: This qualitative reflection study is about the experiences of two technology implementation projects in British Columbia and Ontario, Canada. Researchers from the Telepresence Robot and MouvMat projects participated in four reflection sessions. Using reflexive thematic analysis, we identified themes and gained valuable insight into the experiences, lessons learned, and recommendations. Results: Both projects faced challenges in recruitment, accommodating individual residents' needs and routines, staff shortages and turnover, and logistics barriers due to infrastructural limitations and changing guidelines. The Telepresence Robot and the MouvMat teams implemented a range of adaptive strategies. These included frequent check-ins with families, creating appropriate training materials, co-developing tailored solutions, flexible recruitment approaches, staff engagement tactics, and personalized support. Conclusion: The lessons learned highlighted the need for adaptive strategies in conducting research in LTCHs. The study calls for structural support and partnerships between academics and practice locally, nationally, and internationally, as well as efforts to combat ageism in technology use. Researchers need support for knowledge translation and sharing findings to highlight the value of staff participation and showcase research benefits.
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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 | 0.233 | 0.248 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.020 | 0.029 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.009 | 0.020 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".