Consider the context: An analysis of personal social networks of caregivers of children participating in a paediatric weight management program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it