Evaluation of a collaborative protocolized approach by community pharmacies and general medical practitioners for an Australian minor ailments scheme: study protocol for a cluster-randomized controlled trial (Preprint)
Bibliographic record
Abstract
BACKGROUND Internationally, governments have been investing in supporting pharmacists to take on an expanded role to support self-care for health system efficiency. There is consistent evidence that minor ailment schemes (MAS) promote efficiencies within the healthcare system. The cost savings and health outcomes demonstrated in the UK and Canada opens up new opportunities for pharmacists to effect sustainable changes through MAS delivery in Australia. OBJECTIVE This trial is evaluating the clinical, economic and humanistic impact of an Australian minor ailments service (AMAS), compared with usual pharmacy care in a cluster-randomized controlled trial in Western Sydney, Australia. METHODS The cluster-randomized controlled trial design has an intervention and a control group, comparing individuals receiving a structured intervention with those receiving usual care for specific common ailments. Participants will be community pharmacies, general practices and patients located in Western Sydney Primary Health Network region. 30 community pharmacies will be randomly assigned to either intervention or control group. Each will recruit 24 patients seeking, aged 18 years or older, presenting to the pharmacy in person with a symptom-based or product-based request for one of the following ailments (reflux, cough, common cold, headache (tension or migraine), primary dysmenorrhoea and low back pain). Intervention pharmacists will deliver protocolized care to patients using clinical treatment pathways with agreed referral points and collaborative systems boosting clinician-pharmacist communication. Patients recruited in control pharmacies will receive usual care. The co-primary outcomes are rates of appropriate use of nonprescription medicines and rates of appropriate medical referral. Secondary outcomes include self-reported symptom resolution, time to resolution of symptoms, health services resource utilization and EQ VAS. Differences in the primary outcomes between groups will be analyzed at the individual patient level accounting for correlation within clusters with generalized estimating equations. The economic impact of the model will be evaluated by cost analysis compared with usual care. RESULTS The study began in July 2018. At the time of submission, 30 community pharmacies have been recruited. Pharmacists from the 15 intervention pharmacies have been trained. 27 general practices have consented. Pharmacy patient recruitment began in August 2018 and is ongoing and monthly targets are being met. Recruitment will be completed March 31st, 2019. CONCLUSIONS This study may demonstrate the utilization and efficacy of a protocolized intervention to manage minor ailments in the community, and will assess the clinical, economic and humanistic impact of this intervention in Australian pharmacy practice. Pharmacists supporting patient self-care and self-medication may contribute greater efficiency of healthcare resources and integration of self-care in the health system. The proposed model and developed educational content may form the basis of a MAS national service, with protocolized care for common ailments using a robust framework for management and referral. CLINICALTRIAL Registered with Australian New Zealand Clinical Trials Registry (ANZCTR) and allocated the ACTRN: ACTRN12618000286246. Registered on 23 February 2018.
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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 | 0.079 | 0.069 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.067 | 0.012 |
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".