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Record W2331896342 · doi:10.1186/s13063-016-1308-2

The effectiveness of Technology-assisted Cascade Training and Supervision of community health workers in delivering the Thinking Healthy Program for perinatal depression in a post-conflict area of Pakistan – study protocol for a randomized controlled trial

2016· article· en· W2331896342 on OpenAlexfundno aff
Shamsa Zafar, Siham Sikander, Syed Usman Hamdani, Najia Atif, Parveen Akhtar, Huma Nazir, Joanna Maselko, Atıf Rahman

Bibliographic record

VenueTrials · 2016
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentGrand Challenges CanadaGovernment of Canada
KeywordsMedicineCompetence (human resources)Edinburgh Postnatal Depression ScaleMental healthPsychological interventionImplementation researchNursingRandomized controlled trialDepression (economics)Intervention (counseling)PsychiatryPsychologyCognitionDepressive symptoms

Abstract

fetched live from OpenAlex

BACKGROUND: Rates of perinatal depression in low and middle income countries are reported to be very high. Perinatal depression not only has profound impact on women's health, disability and functioning, it is associated with poor child health outcomes such as pre-term birth, under-nutrition and stunting, which ultimately have an adverse trans-generational impact. There is strong evidence in the medical literature that perinatal depression can be effectively managed with psychological treatments delivered by non-specialists. Our previous research in Pakistan led to the development of a successful perinatal depression intervention, the Thinking Healthy Program (THP). The THP is a psychological treatment delivered by community health workers. The burden of perinatal depression can be reduced through scale-up of this proven intervention; however, training of health workers at scale is a major barrier. To enhance access to such interventions there is a need to look at technological solutions to training and supervision. METHODS/DESIGN: This is a non-inferiority, single-blinded randomized controlled trial. Eighty community health workers called Lady Health Workers (LHWs) working in a post-conflict rural area in Pakistan (Swat) will be recruited through the LHW program. LHWs will be randomly allocated to Technology-assisted Cascade Training and Supervision (TACTS) or to specialist-delivered training (40 in each group). The TACTS group will receive training in THP through LHW supervisors using a tablet-based training package, whereas the comparison group will receive training directly from mental health specialists. Our hypothesis is that both groups will achieve equal competence. Primary outcome measure will be competence of health workers at delivering THP using a modified ENhancing Assessment of Common Therapeutic factors (ENACT) rating scale immediately post training and after 3 months of supervision. Independent assessors will be blinded to the LHW allocation status. DISCUSSION: Women living in post-conflict areas are at higher risk of depression compared to the general population. Implementation of evidence-based interventions for depression in such situations is a challenge because health systems are weak and human resources are scarce. The key innovation to be tested in this trial is a Technology-assisted Cascade Training and Supervision system to assist scale-up of the THP. TRIAL REGISTRATION: Registered with ClinicalTrials.gov as GCC-THP-TACTS-2015, Identifier: NCT02644902 .

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)0.018
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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.034
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.016
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0120.006
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0340.004

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 head0.100
GPT teacher head0.461
Teacher spread0.361 · 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 designRandomized trial
Domainnot available
GenreProtocol

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

Citations41
Published2016
Admission routes1
Has abstractyes

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