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Record W4386019789 · doi:10.2196/47298

Clinicians’ Perspectives and Proposed Solutions to Improve Contraceptive Counseling in the United States: Qualitative Semistructured Interview Study With Clinicians From the Society of Family Planning

2023· article· en· W4386019789 on OpenAlexvenueaboutno aff
Rose Goueth, Kelsey Holt, Karen Eden, Aubri Hoffman

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsnot available
FundersNational Institutes of HealthU.S. National Library of MedicineSociety of Family Planning
KeywordsDecision aidsFocus groupMedicineQualitative researchNeeds assessmentNursingFamily medicineReproductive healthMedical educationFamily planningHealth careInformation needsPopulationPsychologyAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Contraceptive care is a key element of reproductive health, yet only 12%-30% of women report being able to access and receive the information they need to make these complex, personal health care decisions. Current guidelines recommend implementing shared decision-making approaches; and tools such as patient decision aid (PtDA) applications have been proposed to improve patients' access to information, contraceptive knowledge, decisional conflict, and engagement in decision-making and contraception use. To inform the design of meaningful, effective, elegant, and feasible PtDA applications, studies are needed of all users' current experiences, needs, and barriers. While multiple studies have explored patients' experiences, needs, and barriers, little is known about clinicians' experiences, perspectives, and barriers to delivering contraceptive counseling. OBJECTIVE: This study focused on assessing clinicians' experiences, including their perspectives of patients' needs and barriers. It also explored clinicians' suggestions for improving contraceptive counseling and the feasibility of a contraceptive PtDA. METHODS: Following the decisional needs assessment approach, we conducted semistructured interviews with clinicians recruited from the Society of Family Planning. The Ottawa Decision Support Framework informed the interview guide and initial codebook, with a specific focus on decision support and decisional needs as key elements that should be assessed from the clinicians' perspective. An inductive content approach was used to analyze data and identify primary themes and suggestions for improvement. RESULTS: Fifteen clinicians (12 medical doctors and 3 nurse practitioners) participated, with an average of 19 years of experience in multiple regions of the United States. Analyses identified 3 primary barriers to the provision of quality contraceptive counseling: gaps in patients' underlying sexual health knowledge, biases that impede decision-making, and time constraints. All clinicians supported the development of contraceptive PtDAs as a feasible solution to these main barriers. Multiple suggestions for improvement were provided, including clinician- and system-level training, tools, and changes that could support successful implementation. CONCLUSIONS: Clinicians and developers interested in improving contraceptive counseling and decision-making may wish to incorporate approaches that assess and address upstream factors, such as sexual health knowledge and existing heuristics and biases. Clinical leaders and administrators may also wish to prioritize solutions that improve equity and accessibility, including PtDAs designed to provide education and support in advance of the time-constrained consultations, and strategic training opportunities that support cultural awareness and shared decision-making skills. Future studies can then explore whether well-designed, user-centered shared decision-making programs lead to successful and sustainable uptake and improve patients' reproductive health contraceptive decision-making.

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)0.020
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0090.006
Scholarly communication0.0040.003
Open science0.0020.005
Research integrity0.0020.004
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 head0.505
GPT teacher head0.596
Teacher spread0.091 · 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 designQualitative
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

Citations3
Published2023
Admission routes2
Has abstractyes

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