Paramedic Transition into an Academic Role in Universities: A Demographic and Qualification Survey of Paramedic Academics in Australia and New Zealand
Bibliographic record
Abstract
Abstract Objectives To identify the demographic and qualification characteristics of paramedic academics holding teaching and research positions at universities in Australia and New Zealand offering entry-level undergraduate or postgraduate degree programs in paramedicine. Methods A 17 item online normative internet survey was used to obtain demographic and qualification characteristics about the target group. The survey was divided into five categories: demographic data, professional qualifications, educational qualifications, learning and teaching experience, and level of academic skills. Data were collected over a two-month period in 2013 and then collated and reported utilising the capabilities of the Survey Monkey program. Results Of the estimated 66 eligible participants, 30 responded to the survey, 70% were male, the average age when entering academia was 43 years, and the average age when initially entering paramedicine was 23 years. Two-thirds completed their paramedic training in Australia and New Zealand, with the other third training in the UK, US, or Canada. There was a wide-range of levels of training and qualification reported with three having a PhD on entering academia, while most had little to no experience in research, academic writing, and publication. Conclusions Issues of the transference of cultural and professional capital from one community of practice (CoP) into another, the variance in the levels of academic qualifications amongst paramedics when entering academia, and the resources needed to mentor and educate a large majority of these new academics pose significant challenges to new academics and the universities employing them. Key words: paramedicine, university, degree, transition, role
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".