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Record W2516510940 · doi:10.1371/journal.pone.0160683

Comparing Effectiveness of Active and Passive Client Follow-Up Approaches in Sustaining the Continued Use of Long Acting Reversible Contraceptives (LARC) in Rural Punjab: A Multicentre, Non-Inferiority Trial

2016· article· en· W2516510940 on OpenAlexaff
Waqas Hameed, Syed Khurram Azmat, Moazzam Ali, Muhammad Ishaque, Ghazunfer Abbas, Erik Munroe, Rebecca A. Harrison, Wajahat Hussain Shamsi, Ghulam Mustafa, Omar Khan, Safdar Ali, Aftab Ahmed

Bibliographic record

VenuePLoS ONE · 2016
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsHospital for Sick Children
FundersWorld Health OrganizationDavid and Lucile Packard Foundation
KeywordsMedicineDiscontinuationLong-acting reversible contraceptionRandomized controlled trialFamily medicineFamily planningPhysical therapyDemographyPopulationSurgeryEnvironmental healthResearch methodology

Abstract

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BACKGROUND: The use of long-acting reversible contraceptive (LARC) methods is very low in Pakistan with high discontinuation rates mainly attributed to method-related side effects. Mixed evidence is available on the effectiveness of different client follow-up approaches used to ensure method continuation. We compared the effectiveness of active and passive follow-up approaches in sustaining the use of LARC-and within 'active' follow-up, we further compared a telephone versus home-based approach in rural Punjab, Pakistan. METHODS: This was a 12-month multicentre non-inferiority trial conducted in twenty-two (16 rural- and 6 urban-based) franchised reproductive healthcare facilities in district Chakwal of Punjab province, between November 2013 and December 2014. The study comprised of three groups of LARC clients: a) home-based follow-up, b) telephone-based follow-up, and c) passive or needs-based follow-up. Participants in the first two study groups received counselling on scheduled follow-up from the field workers at 1, 3, 6, 9, and 12 month post-insertion whereas participants in the third group were asked to contact the health facility if in need of medical assistance relating to LARC method use. Study participants were recruited with equal allocation to each study group, but participants were not randomized. The analyses are based on 1,246 LARC (intra-uterine contraceptive device and implant) users that completed approximately 12-months of follow-up. The non-inferiority margin was kept at five percentage points for the comparison of active and passive follow-up and six percentage points for telephone and home-based approach. The primary outcome was cumulative probability of method continuation at 12-month among LARC users. RESULTS: Women recruited in home-based, telephone-based, and passive groups were 400, 419 and 427, respectively. The cumulative probability of LARC continuation at 12 month was 87.6% (95% CI 83.8 to 90.6) among women who received home-based follow-up; 89.1% (95% CI 85.7, 91.8) who received telephone-based follow-up; and 83.8% (95% CI 79.8 to 87.1) who were in the passive or needs-based follow-up group. The probability of continuation among women who were actively followed-up by field health educators-either through home-based visit or telephone-based follow-up was, 88.3% (95% CI 85.9 to 90.0). An adjusted risk difference of -4.1 (95% CI -7.8 to -0.28; p-value = 0.035) was estimated between active and passive follow-up. Whereas, within the active client follow-up, the telephone-based follow-up was found to be as effective as the home-based follow-up with an adjusted risk difference of 1.8 (95% CI -2.7 to 6.4; p-value = 0.431). CONCLUSION: A passive follow-up approach was 5% inferior to an active follow-up approach; whereas telephone-based follow-up was as effective as the home-based visits in sustaining the use of LARC, and was far more resource efficient. Therefore, active follow-up could improve method continuation especially in the critical post-insertion period.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.069
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.110
GPT teacher head0.293
Teacher spread0.183 · 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

Citations23
Published2016
Admission routes1
Has abstractyes

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