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Record W2947114447 · doi:10.2196/12233

Using a Call Center to Reduce Harm From Self-Administration of Reproductive Health Medicines in Bangladesh: Interrupted Time-Series

2019· article· en· W2947114447 on OpenAlexvenueno aff
Katherine Keenan, Katharine Footman, Munnaf Sadekin, Kate Reiss, Reena Yasmin, Hannah Franklin, Kathryn Church

Bibliographic record

VenueJMIR Public Health and Surveillance · 2019
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMisoprostolPharmacyAbortionReproductive healthMedical abortionMedical emergencyFamily medicinePopulationPregnancyEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Annually, there are approximately 25 million unsafe abortions, and this remains a leading cause of maternal morbidity and mortality. In settings where abortion is restricted, women are increasingly able to self-manage abortions by purchasing abortion medications such as misoprostol and mifepristone (RU-486) from pharmacies or other drug sellers. Better availability of these drugs has been shown to be associated with reductions in complications from unsafe abortions. In Bangladesh, abortion is restricted; however, menstrual regulation (MR) was introduced in the 1970s as an interim method of preventing pregnancy. Pharmacy provision of medications for MR is widespread, but customers purchasing these drugs from pharmacies often do not have access to quality information on dosage and potential complications. OBJECTIVE: This study aimed to describe a call center intervention in Bangladesh, and assess call center use over time and how this changed when a new MR product (combined mifepristone-misoprostol) was introduced into the market. METHODS: In 2010, Marie Stopes Bangladesh established a care provider-assisted call center to reduce potential harm from self-administration of MR medications. The call center number was advertised widely in pharmacies and on MR product packaging. We conducted a secondary analysis of routine data collected by call center workers between July 2012 and August 2016. We investigated the reported types of callers, the reason for call, and reported usage of MR products before and after November 2014. We used an interrupted time series (ITS) analysis to formally assess levels of change in caller characteristics and reasons for calling. RESULTS: Over the 4-year period, 287,095 calls about MR were received and the number of users steadily increased over time. The most common callers (of 287,042 callers) were MR users (67,438, 23.49%), their husbands (65,999, 22.99%), pharmacy workers (65,828, 22.93%), and village doctors (56,036, 19.52%). Most MR calls were about misoprostol, but after November 2014, a growing proportion of calls were about the mifepristone-misoprostol regimen. The most common reasons (of 287,042 reasons) for calling were to obtain information about the regimen (208,605, 72.66%), to obtain information about side effects (208,267, 72.54%), or to report side effects (49,930, 17.39%). The ITS analyses showed that after November 2014, an increasing number of calls were from MR users who had taken the complete regimen (P=.02 and who were calling to discuss reported side effects (P=.01) and pain medication (P=.01), and there were fewer calls asking about dosages (P<.001). CONCLUSIONS: The high call volume suggests that this call center intervention addressed an unmet demand for information about MR medications from both MR users and health care providers. Call center interventions may improve the quality of information available by providing information directly to MR users and drug sellers, and thus reducing the potential harm from self-management of MR medications.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.089
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.357
Teacher spread0.315 · 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.

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

Citations10
Published2019
Admission routes1
Has abstractyes

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