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Record W2940956062 · doi:10.2196/13387

Feasibility and Acceptability of a Mobile Technology Intervention to Support Postabortion Care in British Columbia: Phase I

2019· article· en· W2940956062 on OpenAlexaffabout
Roopan Gill, Gina Ogilvie, Wendy V. Norman, Brian Fitzsimmons, Ciana Maher, Regina Renner

Bibliographic record

VenueJournal of Medical Internet Research · 2019
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsWomen's Health Research InstituteUniversity of British Columbia
Fundersnot available
KeywordsPsychological interventionAbortionMobile phoneMedicineMobile technologyNursingHealth careFamily medicinemHealthMobile devicePregnancyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Over 30% of women in Canada undergo an abortion. Despite the prevalence of the procedure, stigma surrounding abortion in Canada leads to barriers for women to access this service. The vast majority of care is concentrated in urban settings. There is evidence to support utilization of innovative mobile and other technology solutions to empower women to safely and effectively self-manage aspects of the abortion process. This study is part 1 of a 3-phase study that utilizes user-centered design methodology to develop a digital health solution to specifically support follow-up after an induced surgical abortion. OBJECTIVE: This study aimed to (1) understand how women at 3 surgical abortion clinics in an urban center of British Columbia utilize their mobile phones to access health care information and (2) understand women's preferences of content and design of an intervention that will support follow-up care after an induced abortion, including contraceptive use. METHODS: The study design was based on development-evaluation-implementation process from Medical Research Council Framework for Complex Medical Interventions. This was a mixed-methods formative study. Women (aged 14-45 years) were recruited from 3 urban abortion facilities in British Columbia who underwent an induced abortion. Adaptation of validated surveys and using the technology acceptance model and theory of reasoned action, a cross-sectional survey was designed. Interview topics included demographic information; type of wireless device used; cell phone usage; acceptable information to include in a mobile intervention to support women's abortion care; willingness to use a mobile phone to obtain reproductive health information; optimal strategies to use a mobile intervention to support women; understand preferences for health information resources; and design qualities in a mobile intervention important for ease of use, privacy, and security. Responses to questions in the survey were summarized using descriptive statistics. Qualitative analysis was conducted with NVivo using a thematic analysis approach. This study was approved by the local ethics board. RESULTS: A waiting-room survey was completed by 50 participants, and semistructured interviews were completed with 8 participants. The average age of participants was 26 years. Furthermore, 94% (47/50) owned a smartphone, 85% (41/48) used their personal phones to go online, and 85% would use their cell phone to assist in clinical care. Qualitative analysis demonstrated that women prefer a comprehensive website that included secure email or text notifications to provide tools and resources for emotional well-being, contraceptive decision making, general sexual health, and postprocedure care. CONCLUSIONS: A community-based mixed-methods approach allowed us to understand how women use their cell phones and what women desire in a mobile intervention to support their postabortion care. The findings from this formative phase will assist in the development and testing of a mobile intervention to support follow-up care after an induced surgical abortion.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.045
GPT teacher head0.470
Teacher spread0.426 · 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 teacher head, not a consensus.

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

Citations28
Published2019
Admission routes2
Has abstractyes

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