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Record W2609393974 · doi:10.7870/cjcmh-2017-003

Real-Time Needs, Real-Time Care: Creating Adaptive Systems of Community-Based Care for Emerging Adults

2017· article· en· W2609393974 on OpenAlexaffvenueabout
Javeed Sukhera, Jill Lynch, Nancy Wardrop, Kristina Miller

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2017
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsWestern UniversityLondon Health Sciences Centre
Fundersnot available
KeywordsTransformational leadershipMental healthParticipatory action researchCitizen journalismFocus groupCommunity-based participatory researchGrounded theoryConstructivist grounded theoryMental illnessMental health serviceNursingMental healthcarePsychologyMedicineSociologyPsychiatryQualitative researchPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Research indicates a decline in mental health service utilization between the ages of 16 to 25, leaving emerging adults with mental illness at risk for worsening outcomes. The authors utilized a community-based participatory research (CBPR) approach to explore the mental health landscape for youth aged 16–25 in London, Canada. Interviews and focus groups (n = 30) with community and hospital system leaders, youth and caregivers were transcribed and coded using an approach informed by constructivist grounded theory. There was consensus regarding difficulties in the current system including wait times and crisis-driven services leading to powerlessness among youth and caregivers. Solutions include delivery of services through a flexible, real-time system that emphasizes patient and caregiver engagement, youth centric services and recovery-oriented care across the hospital/community continuum. The results highlight that disparate stakeholders agree regarding the need for transformational change shifting away from traditional medical models.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0190.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.086
GPT teacher head0.406
Teacher spread0.320 · 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 teacher head, not a consensus.

Study designQualitative
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

Citations18
Published2017
Admission routes3
Has abstractyes

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