Benchmarking study of hospital libraries
Notice bibliographique
Résumé
Objectives: To assess the current landscape of hospital libraries by collecting benchmarking data from hospital librarians in the U.S. and other countries. Since the last MLA benchmarking survey in 2002 hospital libraries have faced significant changes including downsizing, position and library elimination, and hospital mergers. This survey will provides information to inform the development and implementation of effective advocacy for hospital libraries. Methods: A web-based, anonymous survey was designed to collect information from hospital librarians representing stand-alone hospitals and hospital systems. The 57-question survey was distributed via select list servs, targeting the US and Canada but open to any country. The topic areas covered hospital/health system, library, and library staff demographics; library characteristics and scope of service; interlibrary loan and document delivery; library funding; and library budget. Hospital library benchmarking surveys, including the previous MLA surveys, were reviewed and applicable questions were added. Results: There were a total of 180 respondents but the total number of responses for each question varied. Select results are as follows: of the responding libraries, 67.2% were part of a hospital system; 24.4% had merged with or were bought by another hospital or health system and, of those, 77.1% had acquired 1-5 hospitals in the last 10 years; 77.9% were not for profits; over half (55.2%) had <5,001 FTE in the organization; 56.9% had one library; 47.7% had 1 FTE librarian, 34.9% had 2-5; 82.1% did not or were not able to use social media; 60.7% didn’t have strategic plans; 66.1% belonged to a consortium; 48.2% provided up to 250 search requests a year; 66.3% did not receive funding outside of their organization; 32.5% had budgets for print books totaling less than $1,000; 30.1% had budgets, excluding salaries, of less than $100,000 and 9.7% had budgets over $1M. Conclusions: These findings contribute to the field’s knowledge of hospital library demographics as well as the services provided. The results suggest implications for hospital librarians regarding staffing levels and the depth of services within their unique settings, especially within the context of rapidly expanding health systems.
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 | 0,011 | 0,061 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,008 | 0,015 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».