Understanding how education/support groups help lone mothers
Bibliographic record
Abstract
BACKGROUND: Lone-mother led families are at increased risk of psychosocial disadvantage, social isolation and mental health morbidity. Community-based programs are more accessible for families seeking assistance. We examine the experiences of eight lone mothers participating in a larger randomized controlled trial (RCT) of a community-based education/support group program using mixed methods. METHODS: A purposeful sample of eight mothers participating in the intervention arm of an RCT of community-based support/education groups was selected for the qualitative study. Individual interviews asked mothers about themselves and their relationships with their children before and after the group. Interviews were taped, transcribed and content analysis was used to code and interpret the data. Quantitative data collected in the RCT were used to describe these mothers. RESULTS: Mothers participating in the RCT and qualitative study experienced multiple difficulties, including financial and mood problems. These mothers reported that before participating in the group, they had shared experiences of social isolation, stigma, a sense of failure, poor relationships with their children and difficulties with financial management. After the group, mothers identified improved self-esteem, support from other mothers, improved parenting skills and improved communication with their children as outcomes of group participation. CONCLUSIONS: The qualitative data revealed mothers' perceptions of specific areas that improved by participating in the group. The utility of complementary information provided by qualitative and quantitative methods in understanding program impact, as well as the need for broader assistance is noted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".