Survey of educators’ end-of-life care learning needs in a Canadian baccalaureate nursing programme
Bibliographic record
Abstract
AIM: To examine the potential need for faculty development in end-of-life care (EOLC) of theory and clinical educators in a collaborative bilingual undergraduate nursing programme in a Canadian university. METHOD: A purposive sample of 53 Anglophone and Francophone theory and clinical educators completed the Palliative Care Quiz for Nursing, the Frommelt Attitude Toward Care of the Dying Scale and an adapted Educators Educational Needs Questionnaire (Patterson et al, 1997). RESULTS: Results indicated that educators held positive attitudes towards caring for dying patients and had modest knowledge levels. Participants identified personal educational needs, preferred learning formats, support and barriers to teaching EOLC and to their participation in continuing educational programmes. Strategies to enhance the teaching and learning of EOLC content in the theory and clinical context were suggested. CONCLUSION: Nurse educators require time, opportunities and relevant resources to develop the competencies required to support the theoretical and experiential learning of students in EOLC. Recommendations include a variety of approaches for faculty development initiatives, including face to face and virtual, which allow nurse educators to share expertise.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".