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Changing Health Care Fields: When, Who, and How

2017· article· en· W2765886501 on OpenAlexaff
Jo-Louise Huq, Jaana Woiceshyn

Bibliographic record

VenueAcademy of Management Proceedings · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEmbeddednessHealth careVariety (cybernetics)Leverage (statistics)Agency (philosophy)Action (physics)Meaning (existential)Field (mathematics)SociologyPublic relationsPolitical sciencePsychologySocial scienceLaw

Abstract

fetched live from OpenAlex

Calls for change to fundamentally transform health care fields are increasing. Health care fields are highly institutionalized, meaning that actors in these fields face a paradox of embedded agency. If behavior and action conforms to institutional pressures, when and how and which actors can step away from pressures to pursue and implement change? In this paper, we conduct a review of the empirical literature on institutional change in health care fields to examine when, who, and how intentional change is pursued. Our review of the literature uncovers six themes around intentionally pursuing change. The themes that emerged suggest that changes and actions that fit health care fields’ constellations of logics are better suited to changing long standing arrangements and practices. The review also shows that change involves a variety of actors some that leverage preferred social positions and some that used nuanced change action to overcome other’s embeddedness in health care fields.

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)0.025
metaresearch head score (Gemma)0.043
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: none
Teacher disagreement score0.025
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.043
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0060.030
Scholarly communication0.0240.032
Open science0.0020.005
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0050.001

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 head0.295
Teacher spread0.231 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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