Supporting resident-centred decision-making about transitions from long-term care homes to hospital: a qualitative study protocol
Bibliographic record
Abstract
INTRODUCTION: Burdensome care transitions may occur despite clinicians' engagement in care planning discussions with residents and their family/friend care partners. Conversations about potential hospital transfers can better prepare long-term care (LTC) residents, their families and care providers for future decision-making. Lack of such discussions increases the likelihood of transitions that do not align with residents' values. This study will examine experiences of LTC residents, family/friend care partners and staff surrounding decision-making about LTC to hospital transitions and codesign a tool to assist with transitional decision-making to help prioritise needs and preferences of residents and their care partners. METHODS AND ANALYSIS: This study will use semi-structured needs assessment interviews (duration: 1 hour), content analysis of existing decision support and discussion tools and a codesign workshop series (for residents and care partners, and for staff) at three participating LTC home research sites. This qualitative work will inform the development of a decision support tool that will subsequently be pilot tested and evaluated at three partnering LTC homes in future phases of the project. The study is guided by the Person-centred Practice in Long-term Care theoretical framework. Interview audio recordings will be transcribed verbatim and analysed using reflexive thematic analysis. Participants will be recruited in partnership with three LTC homes in Ottawa, Ontario. Eligible participants will be English or French speaking residents, family/friend care partners or staff (eg, physicians, nurses and personal support workers) who have experienced or been involved in a transition from LTC to hospital. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the Bruyère Health Research Ethics Board (#M16-23-030). Findings will be (1) reported to participating and funding organisations; (2) presented at national and international conferences and (3) disseminated by peer-review publications.
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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 | 0.084 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.038 | 0.008 |
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".