Consider the context: An analysis of personal social networks of caregivers of children participating in a paediatric weight management program
Bibliographic record
Abstract
Social networks influence the health and well-being of children and families. This study aimed to identify and understand the social networks of caregivers of children participating in the KidFit Health and Wellness Clinic, a paediatric weight management program. An egocentric social network analysis was used. Caregivers with children enrolled in KidFit participated in semi-structured interviews by completing a personal network map and discussing the individuals in their social networks and their influence on them and their family. Twenty-two caregivers (90.9% mothers) completed the interview. Four structural patterns were identified within the networks: existence of a core, star-shaped network, well-connected network and existence of multiple clusters. Healthcare providers and family had the most influence within the caregivers' networks. With the exception of healthcare providers, individuals who communicated less frequently with caregivers tended to have less influence on caregivers. Internet resources, activity-related resources and social media were the top three services, resources or supports that caregivers reported accessing. It is important that practitioners working with children and families within paediatric settings recognize the unique sociocultural context of each family. Reconceptualising a care model that includes community and incorporates services, supports and resources beyond the clinic could enhance treatment.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".