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Record W2785910520 · doi:10.2196/resprot.8368

Text-Based Program Addressing the Mental Health of Soon-to-be and New Fathers (SMS4dads): Protocol for a Randomized Controlled Trial

2018· article· en· W2785910520 on OpenAlexvenueno aff
Richard Fletcher, Chris May, John Attia, Craig F. Garfield, Geoff Skinner

Bibliographic record

VenueJMIR Research Protocols · 2018
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthPsychological interventionmHealthRandomized controlled trialMoodAnxietyDistressPsychologyMedicineIntervention (counseling)NursingClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Recent estimates indicating that approximately 10% of fathers experience Paternal Perinatal Depression (PPND) and the increasing evidence of the impact of PPND on child development suggest that identifying and assisting distressed fathers is justified on public health grounds. However, addressing new fathers' mental health needs requires overcoming men's infrequent contact with perinatal health services and their reluctance to seek help. Text-based interventions delivering information and support have the potential to reach such groups in order to reduce the impact of paternal perinatal distress and to improve the wellbeing of their children. While programs utilising mobile phone technology have been developed for mothers, fathers have not been targeted. Since text messages can be delivered to individual mobile phones to be accessed at a time that is convenient, it may provide a novel channel for engaging with "hard-to-reach" fathers in a critical period of their parenting. OBJECTIVE: The study will test the efficacy of SMS4dads, a text messaging program designed specifically for fathers including embedded links to online information and regular invitations (Mood Tracker) to monitor their mood, in order to reduce self-reported depression, anxiety and stress over the perinatal period. METHODS: A total of 800 fathers-to-be or new fathers from within Australia will be recruited via the SMS4dads website and randomized to the intervention or control arm. The intervention arm will receive 14 texts per month addressing fathers' physical and mental health, their relationship with their child, and coparenting with their partner. The control, SMS4health, delivers generic health promotion messages twice per month. Messages are timed according to the babies' expected or actual date of birth and fathers can enroll from 16 weeks into the pregnancy until their infant is 12 weeks of age. Participants complete questionnaires assessing depression, anxiety, stress, and alcohol at baseline and 24 weeks postenrolment. Measures of coparenting and parenting confidence are also completed at baseline and 24 weeks for postbirth enrolments. RESULTS: Participant were recruited between October 2016 and September 2017. Follow-up data collection has commenced and will be completed in March 2018 with results expected in June 2018. CONCLUSIONS: This study's findings will assess the efficacy of a novel text-based program specifically targeting fathers in the perinatal period to improve their depression, anxiety and distress symptoms, coparenting quality, and parenting self-confidence. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12616000261415; https://www.anzctr.org.au/ Trial/Registration/TrialReview.aspx?id=370085 (Archived by WebCite at http://www.webcitation.org/6wav55wII).

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)0.025
metaresearch head score (Gemma)0.023
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 score0.120
Threshold uncertainty score0.401

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.023
Meta-epidemiology (narrow)0.0070.004
Meta-epidemiology (broad)0.0130.005
Bibliometrics0.0030.003
Science and technology studies0.0040.004
Scholarly communication0.0050.004
Open science0.0040.003
Research integrity0.0090.010
Insufficient payload (model declined to judge)0.1200.018

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 head0.342
GPT teacher head0.601
Teacher spread0.259 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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