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Record W4382918474 · doi:10.1177/13591045231184916

Talking ‘Bout Better outcomes for Adolescent Depression: Youth and Caregiver Perspectives on an Integrated Care Pathway for Depression

2023· article· en· W4382918474 on OpenAlexafffund
Amy Gajaria, Andrea Greenblatt, Matthew Prebeg, Jacqueline Relihan, Peter Szatmari, Darren Courtney

Bibliographic record

VenueClinical Child Psychology and Psychiatry · 2023
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoInstitute for Clinical Evaluative SciencesCentre for Addiction and Mental Health
FundersCundill Centre for Child and Youth Depression
KeywordsThematic analysisFocus groupDepression (economics)PsychologyService providerBest practiceService (business)MedicineClinical psychologyQualitative research

Abstract

fetched live from OpenAlex

BACKGROUND: Depression is a common condition among adolescents, with rates continuing to rise. A gap exists between evidence-based recommendations for the treatment of depression and clinical practice. Integrated Care Pathways (ICPs) can help address this gap, but to date no study has examined how young people and their caregivers experience ICPs and whether these pathways are an acceptable form of care. This study used focus groups with adolescents, caregivers, and service providers to examine experiences of an ICP. METHODS: Six individual interviews with service providers, four focus groups with youth, and two focus groups with caregivers were completed. Data was analyzed consistent with Braun & Clarke's Thematic Analysis Framework within an interpretivist paradigm. RESULTS AND CONCLUSION: The study demonstrated that ICPs are acceptable to youth and their caregivers and that ICPs facilitate shared decision making between youth/caregivers and care providers. Findings also indicated that youth are willing to engage with ICPs particularly when there is a trusted clinician involved who helps interpret and tailor the ICP to the young person's experience. Further questions include how to best integrate these into the overall system and how to further tailor these pathways to support youth with diagnostic complexity and treatment resistance.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.003
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.003
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 head0.045
GPT teacher head0.380
Teacher spread0.335 · 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 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

Citations5
Published2023
Admission routes2
Has abstractyes

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