International Survey of Peritoneal Dialysis Training Programs
Notice bibliographique
Résumé
OBJECTIVE: To survey nurses around the world about current practices for peritoneal dialysis (PD) home training programs. DESIGN: Random sampling of nurses to complete a written survey from the International Society for Peritoneal Dialysis Nursing Liaison Committee. SETTINGS: United States, Canada, South America (Brazil, Columbia), The Netherlands, Hong Kong. METHODS: Surveys and responses were sent by fax whenever possible, or by regular mail, or hand carried, or conducted by telephone. Results were stratified by geographic areas as well as by cumulative responses and were expressed as medians with ranges. Kruskal-Wallis was used to evaluate differences in responses. Associations between variables were tested with Pearson correlation. Univariate regression analysis was used to evaluate the impact of variables on peritonitis rates. Variables with p < 0.10 were included in a multivariate analysis. RESULTS: A total of 317 nurses responded: 88 in the United States, 46 in Canada, 58 in South America, 58 in Hong Kong, and 67 in The Netherlands. This represented 37% of all surveys distributed. Respondents had a median of 12 years' experience in nephrology (range 1-35 years), but only 31% had a formal background in adult education. Nearly half received their guidance to patient training from a nurse colleague, 11% were guided by a corporate colleague, and 8% were simply self-taught. Clinics responding had a median of 30 PD patients (range 1-400) and reported they trained a median of 8 patients per year (range 0-86). Reported peritonitis rates were a median 0.46 per year or 1 episode every 26 months. Peritonitis rates, however, were not known by 53% of respondents. Total training time per patient had a very wide range of hours, from 6 to 96. There was no correlation between training time and peritonitis rates among the study respondents (p = 0.38), nor with any other variables. CONCLUSIONS: There is wide variation in practices for PD patient training programs within countries and around the world. Training time did not appear to be related to peritonitis rates. Randomized trials of training practices are needed to determine which approaches produce the best outcomes for patients.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».