Logistics practices in healthcare organizations in Bogota
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the current state of logistics practices in healthcare organizations in Bogota, Colombia. Design/methodology/approach The assessment is based on case study research using open interviews, focused interviews, a questionnaire and direct observations as sources of evidence. Seven Colombian health care settings are analyzed: four public hospitals and three private clinics. Cross-case analysis allows the identification of patterns regarding supply management, inventory management, replenishment and use of information and communication technologies. Findings Manual procedures, poor planning, little recognition from top management and a lack of specialized personnel characterize the current situation. Innovative practices with a potential to improve the efficacy of logistics activities are rare, particularly in public hospitals. Research avenues Future research could replicate this study in other Colombian cities, in order to generalize the results to the whole country. It could also be interesting to document successful and less successful implementations of innovative logistics practices in Colombian hospitals to guide and promote their adoption. Research limitations/implications The small number of cases considered, and the fact that the research is concentrated in one city, limits the generalizability of the results. Originality/value To the best of the authors’ knowledge, this research is the first to explore the state of healthcare logistics practices in Colombia.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".