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Developing an integrated performance management and measurement system in healthcare organisations: a Canadian case study

2023· preprint· en· W4388019959 on OpenAlexafffundabout
Anes Ben Fradj, Neila El Asli, Tasseda Boukherroub, Claude Olivier

Bibliographic record

VenueF1000Research · 2023
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of CanadaMitacsÉcole de technologie supérieure
KeywordsOpen peer reviewPlant biologyHealth careHealthcare systemMedicinePhysiologyNeuroscienceBiologyPolitical science

Abstract

fetched live from OpenAlex

This study proposes an approach for developing or improving performance management and measurement systems (PMMSs) for healthcare organisations. First, data is collected to analyse and understand the current organisation's performance management system. Second, the SWOT (Strengths, Weaknesses, Opportunities, Threats) method is used to identify the main aspects of the performance management system to be improved. Third, based on the scientific literature and SWOT analysis, BSC principles are integrated to this performance management system to better align the organisation's performance objectives and indicators with its strategy. Finally, we develop a performance indicator structure and select indicators to be used as well as how these indicators could be integrated and shared with higher hierarchical levels in the organisation by using AHP (Analytic Hierarchy Process). Our approach is applied to the CIUSSS du Centre-Sud-de-l'île-de-Montreal (CCSMTL), a large healthcare network, in the province of Québec, Canada.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
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 head0.140
GPT teacher head0.315
Teacher spread0.175 · 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.

Study designObservational
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

Citations3
Published2023
Admission routes3
Has abstractyes

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