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Record W2791248558 · doi:10.5539/ass.v14n4p90

Organizational Culture Artifacts and Compassionate Human Resources Practices in a Healthcare Organization

2018· article· en· W2791248558 on OpenAlexvenueno aff
Merlín Patricia Grueso Hinestroza, Mónica López-Santamaría, Javier L González, Wilmer Salcedo, Marysella Amaya

Bibliographic record

VenueAsian Social Science · 2018
Typearticle
Languageen
FieldPsychology
TopicStress and Burnout Research
Canadian institutionsnot available
Fundersnot available
KeywordsCompassionOrganizational cultureHealth carePsychologyArtifact (error)Organizational commitmentKnowledge managementBusinessPublic relationsSocial psychologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Compassion in organizations and their determinants is a research topic that is still underdeveloped, in consequence, a study was carried out with a sample of 112 employees in a health services organization in Bogotá (Colombia). To achieve this goal, the Organizational Culture Artifact Questionnaire -OCSA- and the Compassion Organizational Practices Questionnaire were administered. The results show that the organizational culture through its artifacts predicts in a significant way the adoption of compassion organizational practices. In analyzing the type of culture that has the greatest predictive power over compassionate organizational practices, it has been found that progressive culture has the greatest effect, in contrast to traditional culture. The conclusions discuss the practical implications of the study and its limitations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.422
Teacher spread0.371 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2018
Admission routes1
Has abstractyes

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