Administración basada en la evidencia (ABE): Bases y aplicación en la resolución de problemas. Una revisión metodológica y presentación de las conclusiones de la investigación
Bibliographic record
Abstract
The theory of business management and its researches are guidelines for resolving problems in the organizations. It is not a learning source when a member finds an unknown problem. As on many other occasions, corporate management has to go to other scientific disciplines for support to deal with new issues as they arise. In the field of problem solving and conceptual assimilations have occurred in other fields of knowledge such as mathematics, psychology, engineering, etc. But the best proof that we can find a solution to the question based on scientific research is in medical practice. To find the solution normally we can appeal against to your intuition or to others opinion, developing corrective actions, mostly new, whose results lead to new knowledge (Argyris y Schon, 1978). This behavior does not allow making the most of the generated knowledge. Years ago, a philosophy that serves as the scientific basis for clinical decisions is being developed in the medical field British, Canadian and American (although in an incipient worldwide way) for clinical decisions. The Evidence Based Medicine. In this work the bases lay to integrate that philosophy in business management, making it operational and paying particular attention to their application in resolving problems, it is called Evidence-Based Management.
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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 | 0.195 | 0.262 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.028 | 0.016 |
| Science and technology studies | 0.003 | 0.037 |
| Scholarly communication | 0.026 | 0.029 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".