Towards a decision support system for health promotion in nursing
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Notice bibliographique
Résumé
AIMS: This study was designed to investigate what type of models, techniques and data are necessary to support the development of a decision support system for health promotion practice in nursing. Specifically, the research explored how interview data can be interpreted in terms of Concept Networks and Bayesian Networks, both of which provide formal methods for describing the dependencies between factors or variables in the context of decision-making in health promotion. BACKGROUND: In nursing, the lack of generally accepted examples or guidelines by which to implement or evaluate health promotion practice is a challenge. Major gaps have been identified between health promotion rhetoric and practice and there is a need for health promotion to be presented in ways that make its attitudes and practices more easily understood. New tools, paradigms and techniques to encourage the practice of health promotion would appear to be beneficial. Concept Networks and Bayesian Networks are techniques that may assist the research team to understand and explicate health promotion more specifically and formally than has been the case, so that it may more readily be integrated into nursing practice. METHODS: As the ultimate goal of the study was to investigate ways to use the techniques described above, it was necessary to first generate data as text. Textual descriptions of health promotion in nursing were derived from in-depth qualitative interviews with nurses nominated by their peers as expert health promoting practitioners. FINDINGS: The nurses in this study gave only general and somewhat vague outlines of the concepts and ideas that guided their practice. These data were compared with descriptions from various sources that describe health promotion practices in nursing, then examples of a Conceptual Network and a representative Bayesian Network were derived from the data. CONCLUSIONS: The study highlighted the difficulty in describing health promotion practice, even among nurses recognized for their expertise in health promotion. Nevertheless, it indicated the data collection and analysis methods necessary to explicate the cognitive processes of health promotion and highlighted the benefits of using formal conceptualization techniques to improve health promotion practice.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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 découle