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Record W3118203723 · doi:10.1177/1359104520982323

The process of integrating psychology into medical clinics: Pediatric psychology as an example

2020· article· en· W3118203723 on OpenAlexaff
Wendy L. Ward, Allison Smith, Catherine Munns, Shasha Bai

Bibliographic record

VenueClinical Child Psychology and Psychiatry · 2020
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsSubspecialtyPediatric psychologyProcess (computing)Family medicinePatient satisfactionPsychologyMedical servicesHealth careMedicineApplied psychologyMedical educationNursingClinical psychologyComputer science

Abstract

fetched live from OpenAlex

The integration of psychological services in medical settings has numerous benefits but a process for systematic integration and system wide evaluation is needed. A process model was created and evaluated for integrating services in 32 outpatient subspecialty clinics. Levels of satisfaction in caregivers ( n = 98), physicians ( n = 27), and non-physicians ( n = 45) were assessed. Most caregivers rated psychology services at the highest level of satisfaction (85%) and would recommend these services to others (100%). Teammates indicated that services should continue in their clinic (85% non-physician; 96% physician) and have improved patient care (71% non-physician; 81% physician). These findings demonstrate positive outcomes associated with the process model and support its utility in integrating psychology services across a health system.

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)0.008
metaresearch head score (Gemma)0.014
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 score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.063
GPT teacher head0.486
Teacher spread0.422 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

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