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Record W4220920540 · doi:10.5195/jyd.2022.1103

Evaluation of a Health Navigator Pilot Program for Youth in Foster Care

2022· article· en· W4220920540 on OpenAlexaffabout
Stephanie Skourtes, Kyla Brophy, Eva Moore

Bibliographic record

VenueJournal of Youth Development · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsMcGill UniversityUniversity of British Columbia
Fundersnot available
KeywordsMentorshipFoster carePovertyMental healthIntervention (counseling)PsychologyNursingHealth careWelfarePositive Youth DevelopmentMedical educationGerontologyMedicineDevelopmental psychologyPsychiatryPolitical science

Abstract

fetched live from OpenAlex

The Health Navigator Program (HNP) was a pilot health mentor intervention program for youth in British Columbia, Canada, with connections to the provincial child welfare system. In this article, youth participants are referred to as “independent youth” as they are independent of traditional familial care. Children and youth in the foster care system face increased prevalence and risk of physical and mental health challenges with lasting implications throughout adulthood. The cumulative effect of childhood trauma, lack of connections to supportive adults, and structural obstacles such as poverty, racism, and sexism all contribute to creating significant barriers for independent youth navigating the health care system. The HNP was created to address these obstacles and facilitate improved health outcomes for independent youth. Youth from 2 program sites were paired with medical student volunteers who provided advocacy and mentorship. A qualitative process evaluation was undertaken to assess the effectiveness of the HNP in achieving the intended program outcomes. Findings revealed that the independent youth participants increased awareness of their own health needs, gained confidence in navigating the health care system, and had improved short-term health outcomes. Relationship building with a caring adult, outside of a paid professional role, was shown to be the most significant factor in achieving these positive outcomes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.134
GPT teacher head0.390
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes2
Has abstractyes

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