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Record W4401895854 · doi:10.1177/00099228241270035

Telementoring to Strengthen Child and Youth Mental Health Care Capacity in Primary Care Providers: Session Duration and Learning Outcomes

2024· article· en· W4401895854 on OpenAlexafffund
Kathleen Pajer, William Gardner, Carley Ouellette, Michael Cheng, Sarah Bissex, R. W. G. Mackay, Tracey MacLaurin, Hazen Gandy

Bibliographic record

VenueClinical Pediatrics · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsMcMaster UniversityOttawa Public HealthChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersOntario Ministry of Health and Long-Term Care
KeywordsMedicineSession (web analytics)Mental healthDuration (music)Logistic regressionPrimary careNursingMental capacityHealth careFamily medicinePsychiatry

Abstract

fetched live from OpenAlex

Primary care providers (PCPs) report insufficient capacity for child and youth mental health care (CYMH). The telementoring program Project ECHO (Extension for Community Healthcare Outcomes) can build capacity, but 75- to 120-minute sessions are a participation barrier. Using a Lean health care paradigm, we designed a 60-minute session, and compared self-reported CYMH capacity strengthening (10 constructs) and satisfaction between 60- and 90-minute sessions. Pre-post (n = 139) and post-cycle (n = 146) survey data were analyzed using generalized linear mixed-effects logistic regression. Capacity strengthening was demonstrated when analyzing both groups together (all Ps ≤ .002). Session duration did not affect capacity strengthening for 9/10 constructs (all Ps > .05), but medication management development was higher with 90-minute sessions ( P = .002). Satisfaction was high in both groups. The 60-minute ECHO CYMH sessions can be used without negative learning outcomes, but more mentoring may be needed to build capacity for psychopharmacologic treatment.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.056
GPT teacher head0.322
Teacher spread0.266 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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