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Record W7142559466

Ontario Health Teams: Negotiating Social Work Values in an Emerging Integrated Care System

2025· other· en· W7142559466 on OpenAlexaffabout
David Harding

Bibliographic record

VenueYorkSpace (York University) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsYork University
Fundersnot available
KeywordsThematic analysisDisengagement theoryNegotiationSocial workNeoliberalism (international relations)Health careSocial determinants of healthMetropolitan areaEthnography
DOInot available

Abstract

fetched live from OpenAlex

Ontario Health Teams (OHTs) represent Ontario's most recent shift towards integrated care. OHTs are underpinned by a common value framework, the "Quintuple Aim." The present study investigates how the Quintuple Aim enables and constrains the work of value-driven professionals, in this case social workers, working within the OHT system. An institutional ethnography was conducted at a Community Health Centre in a metropolitan OHT. Data was collected during six in-depth interviews with both social workers and management staff. Thematic analyses were performed from which four themes emerged: 1) participants' commitment to health equity, 2) issues with provincial leadership & neoliberalism within OHTs, 3) social workers’ disengagement from OHTs, and 4) an on-going need for service integration. Findings suggest misalignment between the values espoused in the Quintuple Aim and providers’ testimonies, most notably in the domains of health equity and provider experiences. Findings are interpreted through a critical neoliberal lens and particular attention is paid to how performance evaluation targets constitute an unspoken set of values within OHTs. Implications are discussed for social work practice and integrated care practices within OHTs.

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)0.005
metaresearch head score (Gemma)0.006
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 score0.905
Threshold uncertainty score0.687

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0280.019
Scholarly communication0.0080.002
Open science0.0020.009
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.240
Teacher spread0.225 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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