Palliative approach education for rural nurses and health-care workers: a mixed-method study
Bibliographic record
Abstract
BACKGROUND: The aim of this mixed-method study was to evaluate the outcomes of an educational intervention in a palliative approach for rural nurses and health-care workers (HCWs) using a team-based method. METHODS: Pre- and post-test measures using the Palliative Care Nursing Self-Competence (PCSNC) scale and the Self-Perceived Palliative Care Knowledge instrument were used to evaluate learning outcomes. Participant post-test scores were also compared to normative provincial data. FINDINGS: At post-test, HCWs showed statistically significant improvements across 7 of 10 domains in self-perceived competence and 6 of 12 domains in self-perceived knowledge; all scores were equivalent to or better than provincial normative data. Nurses' self-perceived knowledge showed statistically significant improvements in 3 of 12 domains; all post-test scores were equivalent to provincial normative data. Qualitative data indicated improvements in familiarity with the resources available for palliative care and in communication among the nursing team. CONCLUSION: An educational intervention can improve the competence and knowledge of rural HCWs and nurses in a palliative approach.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".