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Record W4200544394 · doi:10.25248/reas.e9290.2021

Evaluation in healthcare organizations: a literature review about innovation assessment

2021· review· en· W4200544394 on OpenAlexaff
Breitner Gomes Chaves, Catherine Briand, Khayreddine Bouabida, Carol Giba Bottger Garcia

Bibliographic record

VenueRevista Eletrônica Acervo Saúde · 2021
Typereview
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité de MontréalInstitut National de Santé Publique du Québec
Fundersnot available
KeywordsPerspective (graphical)Process (computing)Health careField (mathematics)Knowledge managementManagement scienceComputer sciencePsychological interventionProcess managementPsychologyBusinessEngineeringPolitical scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Objective: This article identifies and provides the reader with the basis for evaluating the innovations proposed in healthcare organizations and highlights determinants to consider when implementing them. Bibliographic review: There is no complete, exhaustive, and absolute definition of health evaluation. Several evaluative approaches and tools were identified. They can be adapted and used according to the evaluator's evaluative objectives, paradigms, and theoretical influences. Moreover, essential concepts regarding the implementation of innovations were considered and synthesized, allowing the reader to understand the complexity of this phase and its impact on the success of innovations. Final considerations: Although the evaluative field is broad and has several distinct concepts, this article presents a synthesis of concepts that would support decision-makers in evaluating their organization's innovation process. Furthermore, the present paper enables a better understanding of the risks of success or failure of interventions (or innovation) from a comprehensive perspective of the critical determinants in the implementation phase.

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)0.039
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Bibliometrics, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0390.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0020.031
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.232
GPT teacher head0.578
Teacher spread0.346 · 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; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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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