Knowledge and perceived competence among nurses caring for the dying in long-term care homes
Bibliographic record
Abstract
BACKGROUND: The quality of care provided to dying long-term care (LTC) residents is often inadequate, which may be due to the lack of formal training that LTC staff receive in palliative care (PC). The present cross-sectional study assessed PC knowledge and self-efficacy in ability to provide PC in a sample of registered nurses working in LTC homes. METHOD: A survey was conducted in four LTC homes in October 2009 to June 2010. Nursing staff knowledge of PC was evaluated using the Palliative Care Quiz for Nurses (PCQN). The Self-Efficacy in End-of-Life Care Survey (S-EOLC) was used to measure nursing staff confidence in their ability to provide PC. FINDINGS: Close to 60% of the nursing staff participated (69 of 119). The participants did not score highly on the PCQN: the average correct score ranged from 52.50% to 63.41% across the homes. There were no significant differences between the homes for the mean number of correct responses on the PCQN (P=0.329) or mean scores for the three S-EOLC subscales. Rank ordering of the percentage of correct PCQN answers by item and LTC home demonstrated that similar misconceptions were held across homes. CONCLUSION: Despite their confidence in PC practice, the participants' PC knowledge gap reveals a need for PC training for staff working in LTC homes. The PC education and training provided should both include a gerontological perspective and address the expertise and knowledge already held by staff.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".