Outcomes of collaborative and interdisciplinary palliative education for health care assistants: A qualitative metasummary
Bibliographic record
Abstract
AIM: To explore collaborative and interdisciplinary palliative educational strategies and corresponding outcomes for health care assistants. BACKGROUND: Health care assistants are frontline staff who provide almost all hands-on care to seniors. Health care managers are responsible to provide ongoing support and a working environment conducive to health care assistants' abilities to provide quality, safe, evidence-based care. EVALUATION: A Qualitative metasummary of collaborative and interdisciplinary teaching strategies was conducted following guidelines developed by Sandelowski et al. (). KEY ISSUES: The gold standard of palliative care is interdisciplinary delivery of care, yet education is often monodisciplinary. Furthermore, evaluation of interdisciplinary continuing education is most often subjective via self-report questionnaires. CONCLUSIONS: Findings from 16 articles representing 15 studies conducted between 2007 and 2017 were grouped thematically and summarized. Collaborative or interdisciplinary palliative interventions offer health care assistants opportunities to debrief, build their confidence, and critically reflect on the importance of psychosocial care for patients, family and colleagues. IMPLICATIONS FOR NURSING MANAGEMENT: The metasummary provides six suggestions for managers who are considering implementing a collaborative palliative educational workshop and draws attention to the need to create contextual changes that support health care assistants to enact newly acquired knowledge and skills.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".