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Record W3011568781 · doi:10.26686/wgtn.17143859.v1

How a government strategy of active performance management has influenced District Health Boards' delivery of publicly funded elective services

2019· dissertation· en· W3011568781 on OpenAlexfundno aff
Corinne Gower‐Rousseau

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsnot available
FundersPartenariat Canadien Contre Le CancerOrganisation de Coopération et de Développement Économiques
KeywordsService delivery frameworkAccountabilityGovernment (linguistics)BusinessStakeholderEquity (law)Service (business)Public relationsMedicineMarketingPolitical science

Abstract

fetched live from OpenAlex

New Zealand, like most countries, is limited in the amount of publicly funded non-emergency (elective) medical and surgical services that it can provide to its population. In 2000, the ‘Reduced Waiting Times for Public Hospital Elective Services: Government Strategy’ outlined the systematic approach New Zealand would take with elective service waiting time management. The approach included the Government’s use of active performance management, namely, the setting of accountability and clear performance expectations; the ongoing monitoring, measurement, and reporting of performance; and the management of system change using facilitative networks. Since 2001, District Health Boards (DHBs) have been accountable for implementing government electives policy. The thesis examines how the Government’s strategic use of active performance management has influenced DHBs in their delivery of publicly funded elective services. In order to better understand and evaluate elective service delivery outcomes, (in particular that equity of service access has been achieved), and to evaluate the improvement of health service decision-making, there is a need to understand how decision-makers at the macro, meso, and micro levels of the health system are influenced by performance management practices. The research has examined influence from a multi-stakeholder and performance management system perspective. Methods include interviews with DHB and government stakeholders, review of Nationwide Service Framework and government policy documents, and the analysis of ten years of publicly available DHB performance reports to understand compliance patterns. The research narrative synthesised from study data is interpreted using a blend of neo-institutional meta-theories and institutional logics. The research found the government uses two performance models: an administrative control performance model which relies on information collection, control logic and performance feedback, and a professional services performance model which relies on the management of change using networks. Each DHB has established organisational practices in response to active performance management which are largely concerned with the promotion of DHB legitimacy. The influence of the two performance models and the interests of multiple DHB stakeholders is explained by considering the interplay between fifteen organisational practices, the government institutional logics of Active Performance Management and Service Improvement and the organisational field-level institutional logics of Population Health Management, Service Management, Medical Professional, and Integrated Care. Overall, the research concludes that ‘Active Performance Management’ has made a significant contribution reducing public hospital waiting times. It focuses the attention of DHB service managers who are concerned with mitigating risks of financial penalties and loss of leadership legitimacy. However, there are different ‘supply’ decision-making agendas and criteria operating at different levels of the health system. In particular, it is difficult to lock in appropriate accountability arrangements with primary care, and the strategic use of active performance management has led to tensions between DHB management and hospital specialists. If New Zealand wishes to expand its evaluation of health service delivery to take into account outcomes measures, there needs to be a better understanding of the aggregated impact of performance management practices on the health system.

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)0.019
metaresearch head score (Gemma)0.054
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 score0.098
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.054
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0080.007
Scholarly communication0.0120.004
Open science0.0010.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.067
GPT teacher head0.395
Teacher spread0.328 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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