The Demographic and Clinical Practice Profile of Australian Remote and Industrial Paramedics: Findings from a Workforce Survey
Bibliographic record
Abstract
Introduction A large workforce is employed in remote environments in the Australian mining and fuel sectors. Whereas paramedics are increasingly assuming roles as healthcare providers in these locations, little is known about industrial paramedic practice. The aim of this exploratory study was to better understand the demographics, education, clinical practice and work environment of the Australian paramedic workforce in remote and industrial settings to inform future research and education for the emerging specialty. Methods Web-based respondent driven network sampling was used to recruit remote and industrial paramedics in this cross-sectional descriptive study. A self-administered questionnaire elicited responses (n=111) about participant demographics, work environment, initial and continuing education, and clinical scope of practice. Results Paramedic participants working in remote and industrial settings are predominately male (86.5%) with the majority aged 35 to 44 years (38.7%). Their job titles range widely and include paramedic, intensive care paramedic, industrial, mine and offshore paramedics. Participants report an average of 15.4 years of total healthcare experience and working in the remote or industrial health sector for a mean of 7.1 years, primarily in Western Australia (34.2%). These paramedics often engage in continuing education, with 45% studying at a vocational or tertiary institution at the time of the survey. Most respondents (63.9%) describe their employment as directly or indirectly related to the natural resource sector and 75.7% have experience in remote settings such as camps, mining sites, offshore platforms, vessels or small communities. Most practitioners (59.5%) work in a full-time capacity and can perform core paramedic skills including intravenous cannulation, 12-lead electrocardiogram interpretation, chest needle decompression and restricted drug administration. Additionally, more than 40% of those actively working in the sector report having endotracheal intubation and intraosseous access in their scope of practice. They also administer immunisations, antibiotics and other prescription medications, manage chronic diseases, and perform low acuity skills typically included in a community paramedic role. Conclusion This workforce survey is the first of its kind designed to gain a broader understanding of the paramedic practitioners who work in remote and industrial settings and the characteristics of their work environment. Key areas highlighted by this study serve to inform professional regulators, educators and employers with respect to the skills that remote and industrial paramedics perform and the education that is required to support the evolving specialised practice.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".