Understanding the space of nursing practice in Colombia: A critical reflection on the effects of health system reform
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.031 | 0.061 |
| Scholarly communication | 0.032 | 0.016 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".