Australian podiatrists scheduled medicine prescribing practices and barriers and facilitators to endorsement: a cross-sectional survey
Bibliographic record
Abstract
Abstract Background Non-medical prescribing is one healthcare reform strategy that has the potential to create health system savings and offer equitable and timely access to scheduled medicines. Podiatrists are well positioned to create health system efficiencies through prescribing, however, only a small proportion of Australian podiatrists are endorsed to prescribe scheduled medicines. Since scheduled medicines prescribed by Australian podiatrists are not subsidised by the Government, there is a lack of data available on the prescribing practices of Australian podiatrists. The aim of this research was to investigate the prescribing practices among Australian podiatrists and to explore barriers and facilitators that influence participation in endorsement. Methods Participants in this quantitative, cross-sectional study were registered and practicing Australian podiatrists who were recruited through a combination of professional networks, social media, and personal contacts. Respondents were invited to complete a customised self-reported online survey, developed using previously published research, research team’s expertise, and was piloted with podiatrists. The survey contained three sections: demographic data including clinical experience, questions pertaining to prescribing practices, and barriers and facilitators of the endorsement pathway. Results Respondents (n = 225) were predominantly female, aged 25–45, working in the private sector. Approximately one quarter were endorsed (15%) or in training to become endorsed (11%). Of the 168 non-endorsed respondents, 66% reported that they would like to undertake training to become an endorsed prescriber. The most common indications reported for prescribing or recommending medications include nail surgery (71%), foot infections 474 (88%), post-operative pain (67%), and mycosis (95%). The most recommended Schedule 2 medications were ibuprofen, paracetamol, and topical terbinafine. The most prescribed Schedule 4 medicines among endorsed podiatrists included lignocaine (84%), cephalexin (68%), flucloxacillin (68%), and amoxicillin with clavulanic acid (61%). Conclusion Podiatrists predominantly prescribe scheduled medicines to assist pain, inflammatory, or infectious conditions. Only a small proportion of scheduled medicines available for prescription by podiatrists with endorsed status were reportedly prescribed. Many barriers exist in the current endorsement for podiatrists, particularly related to training processes, including mentor access and supervised practice opportunities. Suggestions to address these barriers require targeted enabling strategies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".