Social prescribing: Moving pediatric care upstream to improve child health and wellbeing and address child health inequities
Bibliographic record
Abstract
Social prescribing is a means for trusted individuals in clinical and community settings to connect people who have non-medical, health-related social needs to non-clinical supports and services within the community through a non-medical prescription. Evaluations of social prescribing programs for the pediatric population have demonstrated statistically significant improvements in participants' mental, physical, and social wellbeing and reductions in healthcare demand and costs. Experts have pointed to the particularly powerful impact of social prescribing on children's mental health, suggesting that it may help to alleviate the strain on the overburdened mental health system. Social prescribing shows promise as a tool to move pediatric care upstream by addressing non-medical, health-related social needs, hence why there is an urgent need to direct more attention towards the pediatric population in social prescribing research, policy, and practice. This demands rapid action by researchers, policymakers, and child health professionals to support advancements in this area.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".