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Record W4296162467 · doi:10.1002/hpm.3579

Towards CR<sup>2</sup> evaluation: Culturally ‐reflexive and ‐responsive evaluation in crises times and beyond

2022· article· en· W4296162467 on OpenAlexaff
Lara Gautier

Bibliographic record

VenueThe International Journal of Health Planning and Management · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal
Fundersnot available
KeywordsReflexivitySociologyAnthropology

Abstract

fetched live from OpenAlex

Abstract More than ever, health services evaluators are in high demand. In such context, evaluation deliverables are time‐ and/or culturally‐sensitive. For the target communities or for those with an interest in the evaluation, the priority is to make sure their voice is prominently featured in evaluation deliverables. This priority coincides with a context where evaluators need to negotiate their positionality, in a context where evaluation deliverables are time‐ and/or culturally‐sensitive. Beyond the mere inclusion of health service users as evaluation stakeholders, how do evaluators position themselves in these discussions? How do they meaningfully navigate this new paradigm in health services evaluation? This issue refers to intangible processes, which can be supported through both natural predisposition and the acquisition of specific skills. Core competencies for credentialled evaluators now all feature the importance of self‐awareness and reflexive thinking; as well as the demonstration of appropriate and respectful verbal and non‐verbal communication skills, and the capacity to identify practice communities' needs and capacity to participate, while recognising, respecting, and responding to aspects of diversity. Culturally‐responsive evaluation is a promising approach to reconcile the world of evaluators and diverse practice communities. Recently, the concept of cultural humility—a reflexive learning process initially designed for frontline workers—has also gained important traction. In this perspective paper, we reflect on the added‐value of combining these two approaches (hence, CR 2 evaluation—reflexive and responsive) to fulfil the promise of patient and community‐centeredness in health service evaluation.

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)0.025
metaresearch head score (Gemma)0.000
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 score0.771
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.216
GPT teacher head0.529
Teacher spread0.313 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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