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The work process and care production in a Brazilian indigenous health service

2017· article· en· W2755163770 on OpenAlexaff
Aridiane Alves Ribeiro, Giovanni Gurgel Aciole, Cássia Irene Spinelli Arantes, Jeff Reading, Donna Kurtz, Lídia Aparecida Rossi

Bibliographic record

VenueEscola Anna Nery · 2017
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Health and Education
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaUniversity of Toronto
Fundersnot available
KeywordsBureaucracyIndigenousNursingPrioritizationWork (physics)Production (economics)Health careService (business)Process (computing)SociologyMedicineBusinessPolitical scienceProcess managementEngineeringPoliticsMarketing

Abstract

fetched live from OpenAlex

Abstract Objective: To understand the constitutive elements of the work process and care production in an Indigenous Health Support Service. Methods: Case study. Systematic observation and semi-structured interviews were conducted in January and February of 2012. The participants were 10 nursing professionals of an Indigenous Health Support Center, located in Mato Grosso do Sul state, Brazil. The work process was used as a conceptual and analytical category. Results: Through interpretative analysis, the data were organized into three categories. The results showed that care production was focused on procedures and guided by rigid institutional rules and bureaucracy. The prioritization of institutional rules and procedures was detrimental to the provision of person-centered care. Conclusion: The temporary employment contracts and rigid bureaucratic organization generated a tense work environment. These aspects do not maximize the efforts of the nursing staff to provide person-centered care.

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)0.003
metaresearch head score (Gemma)0.005
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.006
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.045
GPT teacher head0.430
Teacher spread0.385 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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