The advantages and challenges experienced with the implementation and delivery of community paramedicine programmes: A qualitative reflexive thematic analysis
Bibliographic record
Abstract
Objective This study aims to investigate the experiences of individuals involved in implementing and delivering community paramedicine programmes across several different regions internationally, in order to identify key themes that can inform ongoing development and introduction of community paramedicine programmes. Methods In this study, participants were enlisted through convenience sampling and a snowballing method. They responded to a custom survey emphasising open-ended responses. We employed a qualitative reflexive thematic analysis, utilising an inductive coding technique at the semantic/explicit level. This approach allowed us to discern themes from the participants’ accounts of the programme's implementation and delivery, along with their perceptions of its strengths and challenges. Results Data was collected from 29 participants engaged in the development or delivery of community paramedicine programmes spanning seven countries. Five themes were created: (1) community drives the need, which emphasises the importance of community engagement and flexible response to health needs; (2) working with others, which underscores the necessity of collaboration with stakeholders for integration and relationship maintenance; (3) promotion and communication, focusing on clarifying roles and countering misconceptions; (4) recruitment and support of community paramedics, highlighting the significance of experienced paramedic recruitment and providing support for staff retention; and (5) research and evaluation, stressing the importance of data sharing for patient care and programme evaluation. These themes demonstrate the significance of community-centred approaches, interprofessional practice, and programme evaluation in community paramedicine. Conclusion This study highlights the significance of community-centred approaches, interprofessional practice, and programme evaluation in community paramedicine. These findings can inform policymakers and practitioners in the development and implementation of community paramedicine programmes, ultimately improving the health and well-being of communities across different regions internationally.
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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 | 0.049 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".