Harnessing Phones to Target Pediatric Populations with Socially Complex Needs: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Mobile and smartphones are owned and accessed by many, making them a potentially optimal delivery mechanism to reach pediatric patients with socially complex needs (ie, pediatric populations who face overlapping adversities). OBJECTIVE: To address the specialized needs of youth from such groups, this review synthesized the literature exploring the use of phone-based delivery to access pediatric populations with socially complex needs, targeting mental and behavioral health outcomes. The purpose of this synthesis was to provide recommendations for future research developing phone-based interventions for youth with socially complex needs. METHODS: A trained medical librarian conducted the search strategy in the following databases: PubMed, Scopus, CINAHL, PsycINFO, Cochrane CENTRAL Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Google Scholar. Studies targeting youth with socially complex needs were defined by recruiting samples that were primarily from traditionally underserved populations (ie, sex/gender minorities, racial/ethnic background, low socioeconomic status, rural/remote location, and sexual orientation). A systematic narrative framework was utilized and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed (registration number CRD42020141212). RESULTS: A total of 14 studies met the inclusion criteria, with 3 depicting the use of phones to complete assessment and tracking goals and 11 to intervene on mental and behavioral health targets. CONCLUSIONS: The literature indicates important directions for future research, including (1) involving diverse and representative teens (ie, the likely users of the interventions), stakeholders, and clinical/research staff; (2) integrating evidence-based therapies with minority-focused theories; (3) harnessing mobile device capabilities; and (4) considering and assessing for potential costs in phones as delivery mechanisms. TRIAL REGISTRATION: PROSPERO CRD42020141212; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=141212.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".