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Record W4387532646 · doi:10.1080/14719037.2023.2264873

Managing the performance of healthcare networks: a ‘dance’ between control and collaboration

2023· article· en· W4387532646 on OpenAlexafffund
Jenna M. Evans, Elana Commisso, Agnes Grudniewicz, Jennifer Im, Jérémy Veillard, Gregory Richards

Bibliographic record

VenuePublic Management Review · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsUniversity of OttawaUniversity of TorontoMcMaster University
FundersCancer Care Ontario
KeywordsAmbidexterityDanceCorporate governanceManagement control systemKnowledge managementBusinessControl (management)Network governancePublic relationsProcess managementComputer sciencePolitical scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This qualitative case study examines how the tension between control and collaboration is managed in the relationship between a Network Administrative Organization (NAO) and the 40 inter-organizational service delivery networks it governs using a performance management system. We found that the relationship between the NAO and networks operated as a ‘dance’, with the NAO taking steps to collaborate with and control the networks. We identified three forms of network governance ambidexterity that characterize this dance – structural, goal, and behavioural – and propose a framework of their antecedents, stability, and influencing factors. Network governance ambidexterity may help explain network functioning and performance.

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)0.031
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 score0.025
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.023
Scholarly communication0.0080.010
Open science0.0020.009
Research integrity0.0020.003
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 head0.063
GPT teacher head0.401
Teacher spread0.338 · 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

Citations9
Published2023
Admission routes2
Has abstractyes

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