Identifying educational needs in end-of-life care for staff and families of residents in care facilities
Bibliographic record
Abstract
AIM: the purpose of this article is to describe educational needs in end-of-life (EoL) care for staff and families of residents in long-term care (LTC) facilities in the province of Ontario, Canada. Barriers to providing end-of-life care education in LTC facilities are also identified. DESIGN, SETTING AND PARTICIPANTS: cross-sectional survey of directors of care in all licensed LTC facilities in the province of Ontario, Canada. RESULTS: directors of care from 426 (76.9% response rate) licensed LTC facilities completed a postal-survey questionnaire. Topics identified as very important for staff education included pain and symptom management and communication with family members about EoL care. Priorities for family education included respecting the residents' expressed wishes for care and communication about EoL care. Having sufficient institutional resources was identified as a major barrier to providing continuing education to both staff and families. CONCLUSION: through examining educational needs in EoL care this study identified an environment of inadequate staffing and over-burdened care providers. The importance of increased staffing concomitant with education is a priority for LTC facilities.
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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 | 0.002 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".