Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
In Brief Operational and financial clinical engineering (CE) data from 253 acute care hospitals were analyzed for indicators that are statistically valid and useful for measuring, monitoring, and improving performance. The sample is mostly composed of nonprofit public and religious hospitals and is evenly distributed among major and minor teaching hospitals and nonteaching institutions. Almost all CE departments manage all biomedical equipment and provide technology management support, but only some manage imaging, laboratory, nonmedical devices, and beds. Clinical engineering departments typically use 2.5 full-time-equivalent employees per 100 staffed beds or 1 full-time-equivalent employee per 4000 adjusted discharges. Administrative support is available only at large departments. Most of the CE budget is typically spent on service contracts, whereas approximately 20% is dedicated to internal labor. One scheduled maintenance and 1 repair are typically performed per capital device per year. Although the ratio of total CE expense and total equipment acquisition costs was confirmed to be a good indicator at around 4%, several other denominators also emerged as valid and, perhaps, even more widely available for comparisons, for example, staffed beds, adjusted discharges, and number of capital devices. Overall, CE budget is around 0.5% of the hospital's total operating budget. Because of uneven data quality and impossibility of validation, each indicator should not be used individually for precise benchmarking. On the other hand, when used together, multiple indicators provide not only valuable ballpark comparisons but also insights into deviations that warrant further investigation for potential uniqueness and/or improvement opportunities. Operational and financial clinical engineering (CE) data from 253 acute care hospitals were analyzed for indicators that are statistically valid and useful for measuring, monitoring, and improving performance. The sample is mostly composed of nonprofit public and religious hospitals and is evenly distributed among major and minor teaching hospitals and nonteaching institutions. Almost all CE departments manage all biomedical equipment and provide technology management support, but only some manage imaging, laboratory, nonmedical devices, and beds. Clinical engineering departments typically use 2.5 full-time-equivalent employees per 100 staffed beds or 1 full-time-equivalent employee per 4000 adjusted discharges. Administrative support is available only at large departments. Most of the CE budget is typically spent on service contracts, whereas approximately 20% is dedicated to internal labor. One scheduled maintenance and 1 repair are typically performed per capital device per year. Although the ratio of total CE expense and total equipment acquisition costs was confirmed to be a good indicator at around 4%, several other denominators also emerged as valid and, perhaps, even more widely available for comparisons, for example, staffed beds, adjusted discharges, and number of capital devices. Overall, CE budget is around 0.5% of the hospital's total operating budget. Because of uneven data quality and impossibility of validation, each indicator should not be used individually for precise benchmarking. On the other hand, when used together, multiple indicators provide not only valuable ballpark comparisons but also insights into deviations that warrant further investigation for potential uniqueness and/or improvement opportunities.
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.
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 | 0,010 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,005 |
| 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