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Record W2250858093 · doi:10.1080/00981389.2015.1085483

Self-reported patient psychosocial needs in integrated primary health care: A role for social work in interdisciplinary teams

2016· article· en· W2250858093 on OpenAlexaff
Shelley L. Craig, Rachel Frankford, Kate Allan, Charmaine C. Williams, Celia Schwartz, Andrea Yaworski, Gwen Janz, Sara Malek-Saniee

Bibliographic record

VenueSocial Work in Health Care · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsPsychosocialAnxietyDepression (economics)Social supportPrimary careMedicineSocial workHealth carePrimary health careClinical psychologyPsychologyPsychiatryFamily medicineEnvironmental healthPsychotherapistPopulation

Abstract

fetched live from OpenAlex

Despite being identified as significant determinants of health, depression and anxiety continue to be underdiagnosed and undertreated in primary care settings. This study examined the psychosocial health needs of patients at four urban interdisciplinary primary health teams. Quantitative analysis revealed that nearly 80% of patients reported anxiety and/or depression. Self-reported anxiety and depression was correlated with poor social relationships, compromised health status and underdeveloped problem-solving skills. These findings suggest that social workers have a vital role to play within interdisciplinary primary health teams in the amelioration of factors associated with anxiety and depression.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.024
GPT teacher head0.418
Teacher spread0.394 · 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

Citations38
Published2016
Admission routes1
Has abstractyes

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