Using an analytic auto-netnographic approach to explore the perceptions of paramedics in primary care
Bibliographic record
Abstract
Introduction: Paramedics in the UK are moving from emergency ambulance services into primary care, where they are employed to boost the clinical workforce. Whereas there is emerging research that seeks to understand the contribution of paramedics to the primary care workforce, there is none regarding the perceptions paramedics have regarding their role in primary care. Methods: An analytic auto-ethnography was undertaken, utilising a peripheral membership approach for online communities used by paramedics on Facebook, Reddit and Twitter (now X). Over a 3-month period (December 2021 to February 2022), the primary researcher reflected on the conversations, comments and opinions posted within these communities within a reflexive (immersion) journal, considering them against the context of her own experience. Results: Paramedics in primary care, who are generally isolated due to their geographical isolation from each other, utilise online social spaces to foster a community of practice. These forums are used to discuss their clinical role, education and experiences, as well as to consider their place within the primary care workforce. Conclusion: This is the first application of this methodology within online social spaces utilised by UK paramedics. This article also presents novel use of a peripheral membership approach within an analytic auto-netnography in public online spaces for researcher-practitioners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".