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Record W4405913668 · doi:10.1186/s12889-024-21057-9

Engaging fathers(to-be): a pilot study on the adaptation and programme experience of SMS4baba intervention in Kenya’s informal settlements

2024· article· en· W4405913668 on OpenAlexfundno aff
Vibian Angwenyi, Richard Fletcher, Paul Mwangi, Margaret Kabue, Rachael Odhiambo, Stephen Mulupi, Emmanuel Kepha Obulemire, Eunice Njoroge, Eunice Ombech, Mercy Moraa Mokaya, Moses Wesala, Joyce Marangu, Amina Abubakar

Bibliographic record

VenueBMC Public Health · 2024
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersAga Khan Foundation Canada
KeywordsBiostatisticsInformal settlementsMedicineIntervention (counseling)Public healthHuman settlementEnvironmental healthNursingSocioeconomicsEconomic growthGeographySociology

Abstract

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BACKGROUND: Engaging fathers(to-be) can improve maternal, newborn, and child health outcomes. However, father-focused interventions in low-resource settings are under-researched. As part of an integrated early childhood development pilot cluster randomised trial in Nairobi's informal settlements, this study aimed to test the feasibility of a text-only intervention for fathers (SMS4baba) adapted from one developed in Australia (SMS4dads). METHODS: A multi-phased mixed-methods study, which included an exploratory qualitative phase and pre-post evaluation of the adapted SMS4baba text-only intervention was conducted between 2019 and 2022. Three focus-group discussions (FGDs) with 19 fathers were conducted at inception to inform SMS4baba content development; two post-pilot FGDs with 12 fathers explored the acceptability and feasibility of SMS4baba implementation; and 4 post-intervention FGDs with 22 fathers evaluated SMS4baba programme experiences. In the intervention phase, 72 fathers were recruited to receive SMS4baba messages thrice weekly from late pregnancy until over six months postpartum. A pre-enrolment questionnaire captured fathers' socio-demographic characteristics. Pre-post surveys were administered telephonically, and outcome measures evaluated using a paternal antenatal attachment scale, generalised anxiety disorder scale (GAD-7), patient health questionnaire (PHQ-9), and researcher-developed questionnaire items assessing paternal involvement, childcare and parenting practices. Qualitative data were analysed using a thematic approach. Statistical analysis performed included descriptive statistics, tests of association, and mixed model regression to evaluate outcomes. RESULTS: Fathers perceived SMS4baba messages as educational, instilling new knowledge and reinforcing positive parenting, and helped fathers cope with fatherhood transition. High levels of engagement by reading and sharing the texts was reported, and fathers expressed strong approval of the SMS4baba messages. SMS4baba's acceptability was attributed to modest message frequency and utilising familiar language. Fathers reported examples of behaviour change in their parenting and spousal support, which challenged gendered parenting norms. Pre-post measures showed increased father involvement in childcare (Cohen's d = 2.17, 95%CI [1.7, 2.62]), infant/child attachment (Cohen's d = 0.33, 95%CI [-0.03, 0.69]), and partner support (Cohen's d = 0.5, 95%CI [0.13, 0.87]). CONCLUSION: Our findings provide support for father-specific interventions utilising digital technologies to reach and engage fathers from low-resource settings such as urban informal settlements. Exploration of text messaging channels targeting fathers, to address family wellbeing in the perinatal period is warranted. TRIAL REGISTRATION: This study was part of the integrated early childhood development pilot cluster randomised trial, registered in the Pan African Clinical Trial Registry on 26/03/2021, registration number PACTR202103514565914.

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)0.007
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.148
GPT teacher head0.380
Teacher spread0.232 · 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 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

Citations3
Published2024
Admission routes1
Has abstractyes

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