Pain assessment and pain alleviation practices of ambulance officers in Auckland
Bibliographic record
Abstract
Pain is a common complaint of many patients that ambulance officers encounter. The prevalence of pain in Auckland is unknown. Little is known about how ambulance officers and paramedics assess and treat pain. More options exist for treating pain than previously, but frequency of use is uncertain. A retrospective review of ambulance officer patient report forms was carried out (n=371). Patient information including age, gender, chief complaint, and acuity status were recorded. Presence, location and pain assessment variables, including pain scores were abstracted. Documented treatment of pain was also recorded. Crew qualifications were abstracted. Research questions were targeted towards the prevalence of pain, methods and frequency of pain assessment used, pharmacological and non-pharmacological treatment of pain, and if differences existed in how ambulance officers of different qualifications assessed and treated pain. Pain was reported in 49% of patients. Chest (21%) and abdominal (19%) pain were most common. Ten percent of patients had an initial pain scores documented, the mean pain score being 6.04. Mean follow-up pain score was 2.83. Poor documentation of onset, provokes, quality, radiation, severity, timing and pain scores was evident. Paracetamol was the most frequently administered analgesic (19%). Poor documentation of non-pharmacological treatment noted. Basic life support officers recorded some pain assessments more frequently than other qualifications, but intermediate life support officers recorded pain scores more than others did. Poor documentation about pain assessment and treatment of pain exists. There are opportunities for further education of ambulance officers, especially assessment of pain in paediatric and cognitively impaired patients. Consideration should be given to assessing the patient’s desire for analgesia. This study provides a baseline for future comparison.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".