What patient, clinician, policy and socio-cultural factors are associated with the rise in off-label prescribing of domperidone in British Columbia when used to treat low milk supply?
Bibliographic record
Abstract
This research used a case study approach to study the off-label use of domperidone when it is used to treat low breastmilk supply (LMS) in British Columbia (BC). Off-label prescribing occurs when an approved drug is prescribed for a use for which it has not been approved. Off-label prescribing is prevalent in Canada and many drugs prescribed off-label lack strong evidence of effectiveness. My research objective was to identify the clinician, patient, socio-cultural and policy factors that are contributing to domperidone’s increasing use among breastfeeding mothers in BC. Results from my research were intended to increase the overall understanding of contributors to off-label prescribing in general in order to improve its transparency, safety and effectiveness. My four methodologies included online surveys with BC midwives and family physicians, interviews with breastfeeding mothers who had used domperidone to increase their breastmilk supply and an analysis of Health Canada’s policy response to off-label prescribing. The conceptual framework for the research was a socio-ecological model incorporating patient, clinician, community/institutional, socio-cultural and policy levels of analysis. I concluded that multiple factors at all of these levels have contributed to the rise in the off-label prescribing of domperidone to treat LMS in BC. Main drivers included clinician prescribing practices, knowledge and views of domperidone, patient needs and beliefs and the medicalization of breastfeeding. Policy-related factors appeared to have significant influence on off-label prescribing. Health Canada does not explicitly consider potential off-label uses in drug approval documents such as product monographs nor does it systematically collect and analyse adverse drug reactions from off-label uses to identify safety concerns. Off-label uses with safety concerns are not included in post-market surveillance activities such as Health Canada safety warnings for healthcare providers and the public. I concluded that when a drug is considered for an off-label use it should be subjected to additional scrutiny by prescribers to determine its real need and risk/benefit balance. Health Canada should systematically include off-label uses in all ADR reporting and analysis so that safety issues can be identified and responded to through post-market surveillance activities.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".