Australian podiatrists scheduled medicine prescribing practices and barriers and facilitators to endorsement: a cross-sectional survey
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».