Broadening the Patient Safety Agenda to Include Safety in Long-Term Care
Bibliographic record
Abstract
Key-Informant InterviewsKey informants were selected so that the views of people in diverse groups (e.g., family members, front-line staff, researchers, policy makers and managers) from LTC settings across Canada would be captured.Fourteen key informants, identified by an advisory committee, participated in audiotaped, semi-structured telephone interviews.The purpose of the interviews was to identify safety issues in LTC.These interviews were transcribed verbatim, and a thematic analysis of the transcripts AbstractThe recent patient safety literature has included less of an emphasis on long-term settings than on research in the acute care sector.Recognizing this knowledge gap in our understanding of safety in the long-term care sector, the Canadian Patient Safety Institute, Capital Health (Edmonton) and CapitalCare (Edmonton) have collaborated to create a research and action agenda for improving resident safety in Canadian long-term care settings.This collaboration resulted in the development of a background paper highlighting the current state of the science and 14 key-informant interviews with stakeholders across Canada.The background paper subsequently informed an invitational round-table discussion.Key findings from the key-informant interviews as well as implications for research are described in this article.
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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 | 0.103 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.024 | 0.039 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.020 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".