Perceived changes in competence, relatedness, and autonomy reported by mothers since joining a mom-centered digital community
Bibliographic record
Abstract
Abstract Background Motherhood can profoundly challenge individuals’ well-being. Social media and other digital platforms are promising modalities for reaching and supporting mothers with evidence-based psychoeducation and connection to peers. However, much is unknown about how mothers perceive these online peer communities and their impact on health and well-being. Purpose To describe mothers’ perceptions of the impact of exposure to and engagement with a mom-centered digital community (Momwell) on their well-being. Methods Pregnant persons and mothers exposed to Momwell psychoeducational content and community related to motherhood via social media, podcast, or blog completed an online survey (N=569). Participants reported several perceived changes related to competence, relatedness/connection, and autonomy in decision-making since joining the Momwell community by rating their agreement with a series of questions. Results All but two participants identified as mothers; 45% were either pregnant or within 12 months postpartum. The majority agreed with statements about perceived changes in their lives, well-being, and feelings since joining the Momwell community (82-97%). All participants reported positive changes related to their sense of competence, 99% reported positive changes related to relatedness, and 97% reported positive changes related to autonomy. Conclusion Exposure to psychoeducational content related to motherhood and maternal mental health and peer engagement within a mom-centered community can enhance maternal well-being through positive changes in competence, relatedness, and autonomy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".