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Record W1535612915 · doi:10.1108/ijhcqa-11-2011-0065

The PATH project in eight European countries: an evaluation

2013· article· en· W1535612915 on OpenAlexaff
Jérémy Veillard, Michaela Schiøtz, Ann-Lise Guisset, Adalsteinn Brown, Niek Klazinga

Bibliographic record

VenueInternational Journal of Health Care Quality Assurance · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsUniversity of TorontoCanadian Institute for Health Information
Fundersnot available
KeywordsBenchmarkingPreparednessContext (archaeology)Quality (philosophy)OriginalitySample (material)BusinessPerformance measurementQuality managementProcess managementMedicineNursingOperations managementMarketingPolitical scienceQualitative researchSociologyEngineeringGeography

Abstract

fetched live from OpenAlex

PURPOSE: This paper's aim is to evaluate the perceived impact and the enabling factors and barriers experienced by hospital staff participating in an international hospital performance measurement project focused on internal quality improvement. DESIGN/METHODOLOGY/APPROACH: Semi-structured interviews involving international hospital performance measurement project coordinators, including 140 hospitals from eight European countries (Belgium, Estonia, France, Germany, Hungary, Poland, Slovakia and Slovenia). Inductively analyzing the interview transcripts was carried out using the grounded theory approach. FINDINGS: Even when public reporting is absent, the project was perceived as having stimulated performance measurement and quality improvement initiatives in participating hospitals. Attention should be paid to leadership/ownership, context, content (project intrinsic features) and processes supporting elements. RESEARCH LIMITATIONS/IMPLICATIONS: Generalizing the findings is limited by the study's small sample size. Possible implications for the WHO European Regional Office and for participating hospitals would be to assess hospital preparedness to participate in the PATH project, depending on context, process and structural elements; and enhance performance and practice benchmarking through suggested approaches. ORIGINALITY/VALUE: This research gathered rich and unique material related to an international performance measurement project. It derived actionable findings.

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)0.028
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.141
GPT teacher head0.538
Teacher spread0.397 · 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 designOther design
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
Published2013
Admission routes1
Has abstractyes

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