Learning Organization Profile of Educational Hospitals in Iran: Practice of Organizational Interlocking Systems
Notice bibliographique
Résumé
BACKGROUND: Organizational learning has been identified as necessary for different organizations to improve their performance in the changing and competitive environment. PURPOSE: The main purpose of this research was to specify the learning organization profile of educational and health centers of Tehran and Qazvin Universities of Medical Sciences in Iran. METHODOLOGY: The present research was conducted using a cross-sectional method in the academic year of 2013-2014. A staff of 530 from educational hospitals subordinated to Tehran and Qazvin universities of medical sciences participated in the research. The participants were selected using stratified random sampling. That is to say, a random sample of a proportionate size was selected from each hospital. The instrument for data collection was a Likert-scale questionnaire involving 50 items. The statistical techniques of ANOVA, t-test, Chi-square, correlation coefficients (Pearson and Spearman), and regression were utilized to analyze the data. All of them were performed using the Statistical Package for Social Sciences (SPSS) 16.0 for windows. RESULT: the results indicated that 449 of participants (84.7%) had a B.S. degree and 78 of them (14.7%) had an M.S. or a Ph.D. degree. Among the fivefold dimensions of "Learning Organization" model (Learning, Organization, People, Knowledge, and Technology) in comparison of the two universities, the "people" dimension was the highest-rated dimension with the mean rating of 25.71±8.36 and the "learning" dimension was the lowest-rated dimension with the mean of 25.35±8.04. Comparison between the two universities yielded the result that educational hospitals in Tehran University of medical sciences with the rating of 126.56 had a more complete profile than that of educational hospitals in Qazvin university of medical sciences with the rating of 122.23. CONCLUSION: The hospitals of the two above-mentioned universities were, to a great extent, far from the characteristics of Learning Organization. In light of the massive mission of these centers to maintain and improve the community health and to train the skilled labor force, the centers should embark on updating the data and institutionalizing learning. Furthermore, to modify staff's behavior and performance and to achieve their goals, they should accentuate the importance of acquiring, creating, and transferring knowledge.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».