Understanding experiences and perspectives in addressing unmet social needs of children and families in a paediatric weight management program: a qualitative descriptive study
Bibliographic record
Abstract
OBJECTIVE: The objective is to describe the experiences and perceptions of caregivers who participated in a community systems navigator intervention that addressed unmet social needs. DESIGN, SETTING AND PATIENTS: A qualitative descriptive study with caregivers of children enrolled in a clinical trial addressing unmet social needs of families with children cared for in a tertiary pediatric weight management clinic, through community systems navigation. Participants were asked open-ended questions related to perceptions of social needs screening in clinical settings. Interviews were recorded and analysed using Braun and Clarke's six-phase approach to thematic analysis. RESULTS: Ten parent participants were interviewed. Social needs screening perception and acceptability varied between participants. Social needs screening was comfortable for most but stressful for others. Participants noted that trusting relationships promote comfort with sharing social needs information, and this data should be shared on the electronic health record if accurate and purposeful. They found the online screening tool convenient but thought it could also limit opportunities to elaborate. Some participants noted the intervention of community systems navigation helpful; however, others described the need for more tailored resources. CONCLUSIONS: Screening for unmet social needs in clinical settings is complex and should be family centred, including the consideration of the mode of screening, data sharing in the electronic health record and ensuing interventions. Perspectives of families should drive the design of future larger scale community navigation interventions to address unmet social needs in clinical settings.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".