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Record W4319437340 · doi:10.1080/01609513.2023.2174636

Exploring the Feasibility and Acceptability of an Online Arts-Based Mindfulness Program for Adolescent Mothers

2023· article· en· W4319437340 on OpenAlexaff
Vivian Oystrick, Diana Coholic

Bibliographic record

VenueSocial Work With Groups · 2023
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsLaurentian University
Fundersnot available
KeywordsPsychologyMindfulnessAttendanceDistressIntervention (counseling)PovertyDevelopmental psychologyClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Adolescent mothers experience high levels of psychological distress due to social disadvantage, adversities, and limited supports. These issues were exasperated by the requirement of pandemic stay-at-home orders and the closing of in-person programs and services. Given the risks associated with adolescent mothering and the impact on their children’s developmental functioning, it is imperative that intervention programs are implemented to support these young mothers. There is a dearth of research that explores the feasibility of using online programming with adolescent mothers. This article describes our experiences delivering an arts-based mindfulness program online to adolescent mothers during the COVID-19 pandemic. Several challenges were encountered with respect to engagement and facilitation including high attrition rates and numerous disruptions during programming. Although the participants were motivated and interested in the program, they experienced numerous barriers to attendance and participation. Challenges with respect to technology, parenting, and family life significantly impacted the feasibility of online delivery. Future studies could attempt to address the social inequalities experienced by adolescent mothers to improve engagement and the effectiveness of online programs.

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)0.016
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 score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.179
GPT teacher head0.384
Teacher spread0.205 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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