An Analytic Hierarchy Framework for Evaluating Balanced Scorecards of Healthcare Organizations
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Bibliographic record
Abstract
Abstract Healthcare organizations have been operating in a turbulent environment for years. Pressures from the government and competition as well as escalating costs have driven administrators to search for effective management tools. Balanced scorecard (BSC), a strategic management system, has been implemented in business organizations with success and is gaining acceptance in the not-for-profit and healthcare sectors. Despite potential benefits, there are challenges for implementers of BSC such as judgment biases, information overload, and the synthesis of information. This paper proposes to apply the analytic hierarchy process (AHP) to hospital scorecards in performance assessment. Although AHP could be a time-consuming exercise, it allows participative input in determining a comprehensive measure for comparing performance of healthcare organizations. Résumé Depuis des années, les organisations de soins de santé évoluent dans un environnement difficile. Les pressions gouvernementales, la concurrence et l'envolée des coûts poussent les administrateurs à rechercher des outils de gestion plus efficaces. C'est dans ce cadre que le Tableau de bord équilibré (BSC) a été mis en æuvre. Malgré ses avantages potentiels, le BSC bute sur certains problèmes dont la partialité des jugements, l'excès, et la synthèse des informations. Cette étude applique la méthode de la hiérarchie multicritère aux tableaux de bords des hôpitaux dans la gestion de la performance. Même si l'application de cette méthode peut s'avérer chronophage, elle permet de déterminer une mesure d'ensemble pour la comparaison de la performance des organisations de soins de santé.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it