Palliative paramedicine: Comparing clinical practice through guideline quality appraisal and qualitative content analysis
Bibliographic record
Abstract
BACKGROUND: Palliative care is an emerging scope of practice for paramedicine. The COVID-19 pandemic has highlighted the opportunity for emergency settings to deliver palliative and end-of-life care to patients wishing to avoid intensive life-sustaining treatment. However, a gap remains in understanding the scope and limitations of current ambulance services' approach to palliative and end-of-life care. AIM: To examine the quality and content of existing Australian palliative paramedicine guidelines with a sample of guidelines from comparable Anglo-American ambulance services. DESIGN: We appraised guideline quality using the AGREE II instrument and employed a collaborative qualitative approach to analyse the content of the guidelines. DATA SOURCES: Eight palliative care ambulance service clinical practice guidelines (five Australian; one New Zealand; one Canadian; one United Kingdom). RESULTS: None of the guidelines were recommended by both appraisers for use based on the outcomes of all AGREE II evaluations. Scaled individual domain percentage scores varied across the guidelines: scope and purpose (8%-92%), stakeholder involvement (14%-53%), rigour of development (0%-20%), clarity of presentation (39%-92%), applicability (2%-38%) and editorial independence (0%-38%). Six themes were developed from the content analysis: (1) audience and approach; (2) communication is key; (3) assessing and managing symptoms; (4) looking beyond pharmaceuticals; (5) seeking support; and (6) care after death. CONCLUSIONS: It is important that ambulance services' palliative and end-of-life care guidelines are evidence-based and fit for purpose. Future research should explore the experiences and perspectives of key palliative paramedicine stakeholders. Future guidelines should consider emerging evidence and be methodologically guided by AGREE II criteria.
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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 | 0.272 | 0.481 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".