Examining consensus for a standardised patient assessment in community paramedicine home visits: a RAND/UCLA-modified Delphi Study
Bibliographic record
Abstract
OBJECTIVE: Community paramedicine programme are often designed to address repeated and non-urgent use of paramedic services by providing patients with alternatives to the traditional 'treat and transport' ambulance model of care. We sought to investigate the level of consensus that could be found by a panel of experts regarding appropriate health, social and environmental domains that should be assessed in community paramedicine home visit programme. DESIGN: We applied the RAND/UCLA Appropriateness Method in a modified Delphi method to investigate the level of consensus on assessment domains for use in community paramedicine home visit programme. SETTING AND PARTICIPANTS: We included a multi-national panel of 17 experts on community paramedicine and in-home assessment from multiple settings (paramedicine, primary care, mental health, home and community care, geriatric care). MEASURES: A list of potential assessment categories was established after a targeted literature review and confirmed by panel members. Over multiple rounds, panel members scored the appropriateness of 48 assessment domains on a Likert scale from 0 (not appropriate) to 5 (very appropriate). Scores were then reviewed at an in-person meeting and a finalised list of assessment domains was generated. RESULTS: After the preliminary round of scoring, all 48 assessment domains had scores that demonstrated consensus. Nine assessment domains (18.8%) demonstrated a wider range of rated appropriateness. No domains were found to be not appropriate. Achieving consensus about the appropriateness of assessment domains on the first round of scoring negated the need for subsequent rounds of scoring. The in-person meeting resulted in re-grouping assessment domains and adding an additional domain about urinary continence. CONCLUSION: An international panel of experts with knowledge about in-home assessment by community paramedics demonstrated a high level of agreement on appropriate patient assessment domains for community paramedicine home visit programme. Community paramedicine home visit programme are likely to have similar patient populations. A standardised assessment instrument may be viable in multiple settings.
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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 | 0.365 | 0.359 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".