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Record W2983729485 · doi:10.1186/s40814-019-0520-8

Implementation of mifepristone medical abortion in Canada: pilot and feasibility testing of a survey to assess facilitators and barriers

2019· article· en· W2983729485 on OpenAlexafffundabout
Courtney Devane, Regina Renner, Sarah Munro, Édith Guilbert, Sheila Dunn, Marie-Soleil Wagner, Wendy V. Norman

Bibliographic record

VenuePilot and Feasibility Studies · 2019
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsBritish Columbia Centre of Excellence for Women's HealthCentre Hospitalier Universitaire Sainte-JustineWomen's Health Research InstituteUniversity of TorontoInstitut National de Santé Publique du QuébecUniversity of British Columbia
FundersInstitute of Health Services and Policy ResearchCanadian Institutes of Health ResearchCollege of Family Physicians of CanadaAssociation des pharmaciens du CanadaProvincial Health Services AuthorityInstitute of Population and Public HealthMichael Smith Health Research BC
KeywordsMedical abortionMedicineMisoprostolFamily medicineOperationalizationDelphi methodNursingHealth careService delivery frameworkMedical educationAbortionService (business)BusinessPolitical sciencePregnancy

Abstract

fetched live from OpenAlex

BACKGROUND: Direct primary care provision of first-trimester medical abortion could potentially address inequitable abortion access in Canada. However, when Health Canada approved the combination medication Mifegymiso® (mifepristone 200 mg/misoprostol 800 mcg) for medical abortion in July 2015, we hypothesized that the restrictions to distribution, prescribing, and dispensing would impede the uptake of this evidence-based innovation in primary care. We developed and pilot-tested a survey related to policy and practice facilitators and barriers to assess successful initiation and ongoing clinical provision of medical abortion service by physicians undertaking mifepristone training. Additionally, we explored expert, stakeholder, and physician perceptions of the impact of facilitators and barriers on abortion services throughout Canada. METHODS: In phase 1, we developed a survey using 2 theoretical frameworks: Greenhalgh's conceptual model for the Diffusion of Innovations in health service organizations (which we operationalized) and Godin's framework to assess the impact of professional development on the uptake of new practices operationalized in Légaré's validated questionnaire. We finalized questions in phase 2 using the modified Delphi methodology. The survey was then tested by an expert panel of 25 nationally representative physician participants and 4 clinical content experts. Qualitative analysis of transcripts enriched and validated the content by identifying these potential barriers: physicians dispensing the medication, mandatory training to become a prescriber, burdens for patients, lack of remuneration for mifepristone provision, and services available in my community. To assess the usability and reliability of the online survey, in phase 3, we pilot-tested the survey for feasibility. RESULTS: We developed and tested a 61-item Mifepristone Implementation Survey suitable to study the facilitators and barriers to implementation of mifepristone first-trimester medical abortion practice by physicians in Canada. CONCLUSIONS: Our team operationalized Greenhalgh's theoretical framework for Diffusion of Innovations in health systems to explore factors influencing the implementation of first-trimester medical abortion provision. This process may be useful for those evaluating other health system innovations. Identification of facilitators and barriers to implementation of mifepristone practice in Canada and knowledge translation has the potential to inform regulatory and health system changes to support and scale up facilitators and mitigate barriers to equitable medical abortion provision.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.384

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0070.002
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.269
GPT teacher head0.442
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2019
Admission routes3
Has abstractyes

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