Understanding Complaints about Paramedics: A Qualitative Exploration in a Uk Context
Bibliographic record
Abstract
Introduction This research set out to understand the context and explore the reasons for the disproportionate number of complaints raised against paramedics to the United Kingdom professional regulator – the Health and Care Professions Council – relative to other health professions. Methods This paper reports on qualitative findings from one aspect of a mixed-methods study which included a case analysis, Delphi study and literature review. One-to-one semi-structured interviews conducted with 15 stakeholders drawn from practitioners, educators, representatives and regulators, and three focus groups held with 16 practitioners and service users were used to gain an in-depth understanding of the possible reasons for complaints about paramedic practice. Results Five themes were generated from a thematic analysis of the data: the impact of public perceptions and expectations; the challenges of day-to-day practice; the effect of increasingly pressurised services; the organisational and cultural climate which impacts paramedics’ work; and the evolving nature of the profession. Conclusion This study highlights the complex and changing nature of paramedic practice. It provides an insight into the ways in which the character, practice and environment of the profession contribute to a disproportionate number of complaints.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".