First initiatives in prehospital care-Basing assessments on incomplete preliminary information
Bibliographic record
Abstract
Rationale and aim: Ambulance staff, i.e. registered nurses and assistant nurses, receive assignments from emergency dispatch centres including information on level of priority, address and patient’s care requirements. One problem is that the preliminary information the dispatcher gives to the ambulance staff may be incomplete. The purpose of this study was to determine how ambulance staff base their assessments on incomplete preliminary information when assessing care requirements.Methods: Fifteen ambulance staff working at seven ambulance stations were interviewed for this study. Interviews were transcribed and analysed using content analysis.Results: Incomplete preliminary information means that ambulance staff may be misdirected. This means that if the preliminary information from the dispatcher is incomplete, the ambulance staff need to reassess, and this is perceived to be difficult. Ambulance staff tend to stick to the first initiative that is taken after they receive an alert from the dispatcher.Conclusions: When ambulance staff receive incomplete preliminary information, they need to consider the possibility of conducting a reassessment. Based on the results, there is a need for new procedures to improve preparedness to conduct a reassessment after receiving incomplete preliminary information.
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 | 0.056 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".