Beyond Plan-Do-Study-Act cycle – staff perceptions on facilitators and barriers to the implementation of telepresence robots in long-term care
Bibliographic record
Abstract
BACKGROUND: Quality improvement (QI) programs with technology implementations have been introduced to long-term care (LTC) to improve residents' quality of life. Plan-Do-Study-Act (PDSA) cycle is commonly adopted in QI projects. There should be an appropriate investment of resources to enhance learning from iterative PDSA cycles. Recently, scholars explored possibilities of implementation science (IS) with QI methods to increase QI projects' generalisability and make them more widely applicable in other healthcare contexts. To date, scant examples demonstrate the complementary use of the two methods in QI projects involving technology implementation. This qualitative study explores staff and leadership teams' perspectives on facilitators and barriers of a QI project to implement telepresence robots in LTC guided by the Consolidated Framework for Implementation Research (CFIR). METHODS: We employed purposive and snowballing methods to recruit 22 participants from two LTC in British Columbia, Canada: operational and unit leaders and interdisciplinary staff, including nursing staff, care aides, and allied health practitioners. CFIR was used to guide data collection and analysis. Semi-structured interviews and focus groups were conducted through in-person and virtual meetings. Thematic analysis was employed to generate insights into participants' perspectives. RESULTS: Our analysis identified three themes: (a) The essential needs for family-resident connections, (b) Meaningful engagement builds partnership, and (c) Training and timely support gives confidence. Based on the findings and CFIR guidance, we demonstrate how to plan strategies in upcoming PDSA cycles and offer an easy-to-use tool 'START' to encourage the practical application of evidence-based strategies in technology implementation: Share benefits and failures; Tailor planning with staff partners; Acknowledge staff concerns; Recruit opinion leaders early; and Target residents' needs. CONCLUSIONS: Our study offers pragmatic insights into the complementary application of CFIR with PDSA methods in QI projects on implementing technologies in LTC. Healthcare leaders should consider evidence-based strategies in implementing innovations beyond PDSA cycles.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".