NURTURING FATHER AND DAUGHTER RELATIONSHIPS AND FAMILY WELL-BEING: A COMMUNITY-BASED TRANSFORMATIVE INITIATIVE
Bibliographic record
Abstract
Research supports that the positive involvement of fathers in caregiving activities cultivates family cohesion and fosters the emotional well-being of fathers and their families. However, there are limited community-based resources that support and celebrate the development of nurturing fatherhood and focus on strengthening father-daughter relationships. In collaboration with diverse communities in Calgary, Canada, a local network launched a community-based and culturally relevant program, Honouring Fathers and Daughters, to promote positive fatherhood roles and to celebrate both nurturing fatherhood practices and the significance of father-daughter relationships. Participants (N=65), from ethnically and socioeconomically diverse backgrounds, including 19 fathers, 17 mothers, and 29 children, joined the program. A community-based research approach (CBR) was employed to gather participant responses through feedback forms. Through qualitative analysis, responses indicated three key themes: valuing and appreciating father-daughter relationships, the need for social and cultural spaces for the engagement and transformation of fathers, and the importance of providing opportunities for fathers to learn new strategies for parenting and bonding with their daughters. This article highlights the importance of community-based engagement programs for nurturing father-daughter relationships and provides insights for communities.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".