Notice bibliographique
Résumé
The St. John's region in Newfoundland, Canada had a population of 8435 ≥ 75 years in 1996, with 996 nursing home (NH) beds and 550 supervised care (SC) beds. However, only 116 SC beds were available at this time in the city of St. John's, where the majority of this at risk population lived. A single entry system to these institutions was implemented in 1995. To determine the need for long term care (LTC) two incident cohorts requesting placement were studied in 1995/96 (n=467) and in 1999/00 (n=464). Degree of disability was determined using the Residents Utilization Groups-III Classification (RUGs) and the Alberta Resource Classification System (ARCS). Time to placement and survival were measured. Factors predicting placement into LTC and mortality were determined. To determine the impact of the single entry system, clients of six NHs were assessed in 1997 (n=1044) and in 2003 (n=963). -- The number requiring placement increased from 392 to 431 from 1995/96 to 1999/00, an increase of 10% over four years. The population increase in those ≥ 75 years during this time was 8%. Comparing the two time periods, demographic characteristics were similar in the two incident cohorts. The proportion with no indicators for NH was the same (36%), and the proportion sent to SC was 25 and 28% in 1995/96 and 1999/00, respectively. There was no difference in RUGs classification between the two incident cohorts and the proportion classified as high level of care i.e., 6/7 on ARCS remained the same (22 vs. 23%). NH clients in 2003 differed from those in 1997; in 2003 the mean length of stay was shorter (3.7 vs. 4.5 years); the proportion with no indicators for NH care was smaller (10 vs. 19%); the proportion requiring special care/clinically complex was higher (45 vs. 30%); and the proportion with a low level ARCS i.e., 1/2 was smaller (16 vs. 25%). This suggests that clients admitted to NH care following the start of a single entry system were more appropriately placed than before. Time to placement was unchanged for SC and NH care comparing both time periods. Time to placement in SC was much faster than in NHs. Independent factors which influenced time to placement included residence, RUGs, panel recommendation, sex, and age. Time from panel assessment to death for those recommended for SC was unchanged in both incident cohorts (3.09 vs. 3.02 years), as was those recommended for NH (2.35 vs. 2.23 years). Independent factors that influenced mortality included RUGs, sex and age. Using optimal methods of placement in 1995/96, as defined by a decision tree, the need for NHs decreased (75 to 37%); for SC increased (25 to 37%); and SC for cognitive impairment (CI) was 26%. In 1999/00, the need for NHs decreased (72 to 44%); for SC increased (28 to 36%); and SC for CI was 20%. Using optimal methods of placement, a deficit of 253 SC beds in the city and an excess of 235 outside the city would occur by 2014. An excess of 692 NH beds in the city and a deficit of 164 outside the city will exist. A total of 251 SC beds for the CI are crucial. -- It was concluded that the St. John's region had an excess of NH beds and a geographic imbalance of SC beds leading to over-utilization of NH beds. The single entry system succeeded in improving the appropriateness of utilization of NH beds. Nonetheless, SC facilities for the elderly with modest disability and for those with CI are necessary, as is a reduction in NH beds.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 source (Gemma direct ou Codex distillé), 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 ».