Pharmacists' Participation in an Inhaled Respiratory Medication Program: Reimbursement of Professional Fees
Bibliographic record
Abstract
BACKGROUND: An intervention (termed Initiative) was initiated to facilitate converting beneficiaries of a public drug insurance program in the province of Nova Scotia from respiratory nebulization medications to inhalers. Community pharmacists provided patient education and billed professional fees for conversions or optimizing inhaled respiratory medication technique. OBJECTIVE: To determine community pharmacists' self-reported participation rate and identify facilitators and barriers to billing for professional fees. METHODS: A survey was developed and mailed to Nova Scotia pharmacists. Information on demographics, work environment, professional experience, financial aspects, billing experiences, and the billing process was collected. Quantitative and qualitative data were evaluated using bivariate and multivariate analyses, and a thematic process, respectively. RESULTS: Two hundred ninety-seven pharmacists responded. Self-reported billing rates for fees were 34% (switching delivery devices), 58% (optimizing AeroChamber use), and 37% (follow-up when replacing Aerochambers). Awareness of fees and the perception of consistent claim reimbursement were associated with billing for each fee (p < 0.05). Predisposing billing factors included awareness of fees, identifying situations requiring education, owner/manager position, male gender, perception that billing for education for optimizing technique is a minimum standard of practice, and prescription volume. Themes identified as barriers included inefficient billing process, inadequate fees, and lack of Initiative awareness. CONCLUSIONS: Predisposing factors were the most important facilitators of community pharmacists' participation in this program, while a cumbersome and time-consuming billing process was the primary barrier. Further research should determine the impact of the professional fee on patient health outcomes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".