Paramedic attitudes and experiences working as a community paramedic: a qualitative survey
Bibliographic record
Abstract
Abstract Background Community paramedicine (CP) is an extension of the traditional paramedic role, where paramedics provide non-acute care to patients in non-emergent conditions. Due to its success in reducing burden on hospital systems and improving patient outcomes, this type of paramedic role is being increasingly implemented within communities and health systems across Ontario. Previous literature has focused on the patient experience with CP programs, but there is lack of research on the paramedic perspective in this role. This paper aims to understand the perspectives and experiences, both positive and negative, of paramedics working in a CP program towards the community paramedic role. Methods An online survey was distributed through multiple communication channels (e.g. professional organizations, paramedic services, social media) and convenience sampling was used. Five open-ended questions asked paramedics about their perceptions and experiences with the CP role; the survey also collected demographic data. While the full survey was open to all paramedics, only those who had experience in a CP role were included in the current study. The data was qualitatively analyzed using a comparative thematic analysis. Results Data was collected from 79 respondents who had worked in a CP program. Three overarching themes, with multiple sub-themes, were identified. The first theme was that CP programs fill important gaps in the healthcare system. The second was that they provide paramedics with an opportunity for lateral career movement in a role where they can have deeper patient connections. The third was that CP has created a paradigm shift within paramedicine, extending the traditional scope of the practice. While paramedics largely reported positive experiences, there were some negative perceptions regarding the slower pace of work and the “soft skills” required in the role that vary from the traditional paramedic identity. Conclusions CP programs utilize paramedic skills to fill a gap in the healthcare system, can improve paramedic mental health, and also provide a new pathway for paramedic careers. As a new role, there are some challenges that CP program planners should take into consideration, such as additional training needs and the varying perceptions of CP.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".