Why do paramedics have a high rate of self-referral?
Bibliographic record
Abstract
Paramedics have been regulated in the UK since 2003. Analysis shows that the profession has had consistently higher rates of self-referral to its regulator compared with other health and care professions. Between 2013 and 2016, the percentage of paramedics who self-referred averaged 50% of all cases, compared with 6% across all other health professions regulated by the Health and Care Professions Council (HCPC) and 10% across social workers in England. This article reports on possible reasons underlying this trend. Using a mixed-methods approach including a literature review, interviews, focus groups and case analysis, the study identified a number of possible contributory factors. These included pressurised work environments, variable guidance and support from employers, and work cultures of fear and conflict. The evolving nature of the profession was also cited. The research found that there was a cohort of cases that appeared inappropriate—where the referral was for a matter that did not require reporting. Actions are being taken to reduce such self-referrals to avoid the emotional distress and resource implications for those involved.
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 | 0.005 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".