Paramedic Perceptions of Barriers to Prehospital Oral Analgesia Administration
Bibliographic record
Abstract
Introduction Pre-hospital analgesia is administered at a suboptimal rate. We aimed to identify barriers to pre-hospital oral analgesia administration to adult patients, as perceived by paramedics, using qualitative methods. Methods Paramedics from a county emergency medical service were invited to participate in semi-structured interviews. The interviews consisted of two questions regarding barriers to pre-hospital administration of oral analgesia using a previously established medical directive. The same investigator completed all interviews, which were audio recorded and transcribed verbatim. Barriers to analgesia administration to adult patients were identified from the interview transcripts using open coding of the data. Two investigators completed the coding process independently and discrepancies were then resolved by consensus. Code frequencies were tabulated and thematic analysis was used to organise the data into broad domains and themes. Results In total, 44 paramedics of a possible 46 (95.7%) completed a semi-structured interview. The final sample size was 43 after exclusion criteria were applied. The median paramedic age and practice experience was 39 and 9.5 years respectively, 58% of the participants were male. Barriers to oral analgesia administration emerged in the domains of patient, medical directive, and paramedic factors. Conclusion Paramedic perceived barriers to pre-hospital oral analgesia administration were identified and include those related to patient, medical directive, and paramedic factors. Minimising these barriers should be undertaken to reduce rates of pre-hospital under-treatment of pain and improve pre-hospital pain management.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".