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Record W7033210299

The performance of health systems and hospitals in Canada: Studies on outcomes measurement

2021· dissertation· en· W7033210299 on OpenAlexaboutno aff

Bibliographic record

VenuePure Amsterdam UMC · 2021
Typedissertation
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsnot available
Fundersnot available
KeywordsOutcome (game theory)ScrutinyHealth careHealthcare systemPerformance measurementHealth policyOutcomes researchCorporate governance
DOInot available

Abstract

fetched live from OpenAlex

Scrutiny on the performance of health systems and hospitals in Canada has increased in recent years. Progress has been made away from more rudimentary measures of resources and output performance towards more meaningful measures of outcomes. However, health system and hospital performance outcome measures have inherent limitations and deficiencies to accurately and adequately reflect their contribution towards the realization of health in populations and patients. This dissertation is composed of five research questions and resultant studies on outcomes-based measurement in the Canadian health system and hospital contexts. The specific research questions of this thesis are: 1. What is outcomes measurement and its state of use in Canada? 2. How are health system and hospital performance indicators evaluated and prioritized? 3. What is the validity and impact of the palliative care code on the Hospital Standardised Mortality Ratio (HSMR) indicator? 4. What are the performance trends on hospital outcome indicators? 5. What is the degree of association between hospital performance outcome indicators? The findings of this dissertation pose additional areas of research necessary to better understand and utilize health outcome measures. This dissertation also raises important policy considerations both in terms of gaps in practice for optimizing and introducing health outcome measures, but also for critical review of existing policies and governance models that rely on and involve select outcome measures.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.061
GPT teacher head0.371
Teacher spread0.310 · 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 teacher head, 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
Published2021
Admission routes1
Has abstractyes

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