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Record W2884982953 · doi:10.1111/nin.12242

Understanding the space of nursing practice in Colombia: A critical reflection on the effects of health system reform

2018· article· en· W2884982953 on OpenAlexaff
Pilar Camargo‐Plazas

Bibliographic record

VenueNursing Inquiry · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsQueen's University
Fundersnot available
KeywordsHealth careDehumanizationHarmInjusticeCompetence (human resources)Public relationsNursingMoral responsibilitySociologyPolitical scienceMedicinePsychologyLawSocial psychology

Abstract

fetched live from OpenAlex

Worldwide, healthcare has been touched by neoliberal policies to the extent that it has some of its characteristics, such as being asymmetrical, competitive, dehumanized, and profit driven. In Colombia, Law 100/93 was created as an ambitious reform aimed at integrating the social security and public sectors of healthcare in order to create universal access, and at the same time to generate market competence with the objective of improving effectiveness and responsiveness. Instead, however, Colombian health reform has served to generate competition which has aggravated inequalities among people. Within this context, we practice nursing. As nurses, our responsibility is to advocate for our patients. We cannot ignore what is happening worldwide in hospitals and community health settings because our responsibility is to promote health, prevent disease, and care for human beings. So, today, when the world pushes for economical profit and competence on one hand, and, on the other, for moral compromises to care, respect, and advocacy for all human beings, being a nurse in the Colombian health system represents a challenge for us. This challenge is especially significant because harm and benefit, justice and injustice, respect and disrespect are separated by a fine line that is easy to transgress.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.897
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.186
GPT teacher head0.431
Teacher spread0.244 · 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 designTheoretical or conceptual
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

Citations9
Published2018
Admission routes1
Has abstractyes

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