Quality of work life of paramedics practicing community paramedicine in northern Ontario, Canada: a mixed-methods sequential explanatory study
Bibliographic record
Abstract
INTRODUCTION: This article aimed to evaluate pilot community paramedicine (CP) programs in northern Ontario from the perspectives of paramedics to gain program recommendations related to both rural and urban settings. METHODS: An online questionnaire was created and distributed to 879 paramedics with and without CP experience employed at eight emergency medical services providers in northern Ontario. An explanatory sequential design was used to analyze and synthesize the results from the quantitative survey items and the open-ended responses. RESULTS: Seventy-five (40.5%) respondents participated in a CP program, and the majority of 75 paramedics who indicated they participated in CP (n=41, 54.4%) were from rural areas. CP was generally well received by both paramedics currently practicing CP and those who were not practicing CP. The majority (86.3%) of paramedics stated paramedics should be practicing CP in the future. Paramedics identified developing professional relationships and improving health promotion as positive aspects of CP. Areas for CP program improvement included better organization and scheduling, improved training and a need for better patient tracking software. CONCLUSION: Engaging and consulting paramedics in the ongoing process of CP development and implementation is important to ensure they feel valued and are part of the change process.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".