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Record W2745878983 · doi:10.15171/ijhpm.2017.100

Competing Logics and Healthcare Comment on "(Re) Making the Procrustean Bed? Standardization and Customization as Competing Logics in Healthcare"

2017· letter· en· W2745878983 on OpenAlexaff
Mike Saks

Bibliographic record

VenueInternational Journal of Health Policy and Management · 2017
Typeletter
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStandardizationPleaHealth careMetaphorSet (abstract data type)PersonalizationSociologyHealth professionalsField (mathematics)Public relationsPolitical scienceEngineering ethicsComputer scienceLawMathematicsEngineering

Abstract

fetched live from OpenAlex

This paper offers a short commentary on the editorial by Mannion and Exworthy. The paper highlights the positive insights offered by their analysis into the tensions between the competing institutional logics of standardization and customization in healthcare, in part manifested in the conflict between managers and professionals, and endorses the plea of the authors for further research in this field. However, the editorial is criticized for its lack of a strong societal reference point, the comparative absence of focus on hybridization, and its failure to highlight structural factors impinging on the opposing logics in a broader neo-institutional framework. With reference to the Procrustean metaphor, it is argued that greater stress should be placed on the healthcare user in future health policy. Finally, the case of complementary and alternative medicine is set out which - while not explicitly mentioned in the editorial - most effectively concretizes the tensions at the heart of this analysis of healthcare.

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)0.012
metaresearch head score (Gemma)0.068
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.059
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.068
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0090.011
Scholarly communication0.0100.007
Open science0.0040.003
Research integrity0.0590.056
Insufficient payload (model declined to judge)0.0030.002

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 head0.381
GPT teacher head0.584
Teacher spread0.203 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2017
Admission routes1
Has abstractyes

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