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Record W4403632073 · doi:10.1186/s12913-024-11762-x

In-person vs mobile app facilitated life skills education to improve the mental health of internally displaced persons in Nigeria: protocol for the RESETTLE-IDPs cluster randomized hybrid type 2 effectiveness-implementation trial

2024· article· en· W4403632073 on OpenAlexafffundabout
Ejemai Eboreime, Chisom Obi‐Jeff, Rita Orji, Tunde M. Ojo, Ihoghosa Iyamu, Bala I Harri, Jidda Mohammed Said, Funmilayo Oguntimehin, Abdulrahman Ibrahim, Omolayo Anjorin, Andem Effiong Etim Duke, Umar Baba Musami, Linda Liebenberg, Raquel Crider, Lydia Wagami, Asmau MC Dahiru, Chigozie Jesse Uneke, Sanni Yaya, Vincent I. O. Agyapong

Bibliographic record

VenueBMC Health Services Research · 2024
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsBC Centre for Disease ControlUniversity of British ColumbiaNova Scotia Health AuthorityDalhousie University
FundersCanadian Institutes of Health ResearchForeign, Commonwealth and Development OfficeGrand Challenges CanadaDalhousie UniversityGlobal Affairs CanadaUnited States Agency for International Development
KeywordsNursing researchHealth informaticsHealth administrationMedicineProtocol (science)Mental healthCluster (spacecraft)Public healthNursingPsychiatryAlternative medicineComputer network

Abstract

fetched live from OpenAlex

BACKGROUND: Internally displaced persons (IDPs) in Nigeria face a high burden of mental health disorders, with limited access to evidence-based, culturally relevant interventions. Life skills education (LSE) is a promising approach to promote mental health and psychosocial well-being in humanitarian settings. This study aims to evaluate the effectiveness and implementation of a culturally adapted LSE program delivered through in-person and mobile platforms among IDPs in Northern Nigeria. METHODS: This cluster-randomized hybrid type 2 effectiveness-implementation trial will be conducted in 20 IDP camps or host communities in Maiduguri, Nigeria. Sites will be randomly assigned to receive a 12-week LSE program delivered either through in-person peer support groups or WhatsApp-facilitated mobile groups. The study will recruit 500 participants aged 13 years and older. Intervention effectiveness outcomes include the primary outcome of change in post-traumatic stress disorder (PTSD) symptoms assessed using the PCL-5 scale, and secondary outcomes of depression, anxiety, well-being, and life skills acquisition. Implementation outcomes will be assessed using the Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), and Feasibility of Intervention Measure (FIM). Both sets of outcomes will be compared between the in-person and mobile delivery groups. Quantitative data will be analyzed using mixed-effects linear regression models, while qualitative data will be examined through reflexive thematic analysis. The study will be guided by the Reach-Effectiveness-Adoption-Implementation-Maintenance (RE-AIM) framework. DISCUSSION: The RESETTLE-IDPs study addresses key gaps in the evidence base on mental health interventions for conflict-affected populations. It focuses on underserved IDP populations, evaluates the comparative effectiveness of in-person and mobile-delivered LSE, and incorporates implementation science frameworks to assess contextual factors influencing adoption, fidelity, and sustainability. The study employs a community-based participatory approach to enhance cultural relevance, acceptability, and ownership. Findings will inform the development and scale-up of evidence-based, sustainable mental health interventions for IDPs in Nigeria and other humanitarian contexts. TRIAL SPONSOR: Dalhousie University, 6299 South St, Halifax, NS B3H 4R2, Canada. TRIAL REGISTRATION: ClinicalTrials.gov, NCT06412679 Registered 15 May 2024.

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)0.017
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 score0.060
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.017
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0090.005
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0040.003
Open science0.0040.002
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0600.008

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 head0.039
GPT teacher head0.506
Teacher spread0.468 · 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

Citations5
Published2024
Admission routes3
Has abstractyes

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