Perceptive responses and familiar staff facilitate meaningful engagement of older adults and family/care partners in long-term care home implementation science research during COVID-19
Bibliographic record
Abstract
A novel registered practical nurse-led video conferencing approach using PIECESTM for team-based care planning was developed to engage family/care partners in the care of older adults. The objectives were to: (a) explore the experiences of older adults and family/care partners in collaborating in implementation science research in long-term care (LTC); (b) identify facilitators and barriers to engaging older adults and family/care partners in implementation science research; and (c) share recommendations to support the engagement of older adults and family/care partners in research. A qualitative descriptive design was used. Two older adults and two family/care partners from two Canadian LTC homes were involved in the research. Data, comprised of interviews with older adults and family/care partners, and notes from research team meetings, were analyzed using thematic analysis. Older adults and family/care partners perceived they made valuable contributions to the research project. They expressed beliefs that care delivery required improvements for older adults with responsive behaviours in LTC, which served as motivation to participate in the research project. Facilitating factors included the support of familiar LTC staff for older adults to engage in research activities and understanding the value of PIECES. A barrier to engagement for older adults was research terminology and processes described during team meetings. This research highlighted taken-for-granted factors in a collaborative research endeavour with older adults and family/care partners. One-on-one interaction, follow-up 'reporting' and presence of familiar LTC staff are needed to support meaningful engagement of older adults and family/care partners in research. Experience Framework This article is associated with the Innovation & Technology lens of The Beryl Institute Experience Framework (https://theberylinstitute.org/experience-framework/). Access other PXJ articles related to this lens. Access other resources related to this lens.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".