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Enregistrement W1701911414 · doi:10.18438/b8qg6c

The Quality of Academic Library Building Improvements Has a Positive Impact on Library Usage

2006· article· en· W1701911414 sur OpenAlexaffvenue
Julie McKenna

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2006
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueLibrary Science and Administration
Établissements canadiensUniversity of Regina
Organismes subventionnairesnon disponible
Mots-clésComputer scienceSpace (punctuation)Quality (philosophy)PopulationWeb surveyAcademic libraryWorld Wide WebData collectionLibrary scienceMedicineMathematicsStatistics

Résumé

récupéré en direct d'OpenAlex

A review of: Shill, Harold B. and Shawn Tonner. “Does the Building Still Matter? Usage Patterns in New, Expanded, and Renovated Libraries, 1995-2002.” College & Research Libraries 65.2 (Mar. 2004): 123-150. Objective – To measure the impact of academic library facility improvements on physical library usage. Design – The facility improvement data used for this study were previously collected through a 68-item Web survey for the companion article “Creating a Better Place: Physical Improvements in Academic Libraries, 1995-2002” (Shill and Tonner). The measurement of library usage was by exit gate counts before and after library improvements. Setting – American academic libraries in which: facility improvement projects were completed between 1995 and 2002, the project space was not smaller than 20,000 square feet, the project space did not include off-site storage or non-public space, and gate-count statistics from before and after facility changes were available. Subjects – Ninety of 384 identified academic libraries were able to provide usable data on: exit gate count, total circulation, in-house collection use, and reference transaction data. Methods – The data collection was undertaken in 2003 for the companion study (Shill and Tonner). A population of 384 libraries potentially able to meet criteria for the study was gathered and each library was invited by e-mail to complete a Web-based survey. Through this initial contact, 357 libraries were confirmed as meeting the study criteria, and responses were received from 182 of those providing a 51% overall response rate. Respondents were asked about institutional characteristics (public or private, Carnegie classification, etc.); project specific features (year of completion, nature of project, etc.); nature and extent of changes (seating, wiring, HVAC, etc.); presence of non-library services in the facility; collection arrangements; before and after quality changes in lighting, seating and a range of services (as assessed by the survey respondent); and before and after project completion gate count usage statistics. Respondents were asked a set of eleven questions each with a five-point scale about facility quality and librarian satisfaction with the former and the changed facility. A further criteria requirement of the availability of pre- and post-project gate count was implemented, reducing the number of libraries to be studied to 90. Facility usage changes were calculated by subtracting the gate count total for the last complete year pre-project from the most recent year gate count post project. Main results - Eighty percent of the 90 libraries reported increased gate count post-project, and 20 percent reported a decline in usage. The median increase across the libraries was 37.4 percent with 25.6 percent of libraries experiencing a post-project increase of 100 percent or more. Renovated facilities were more likely to see usage decline, but there was no statistically significant difference in usage change between renovated and new facilities. Libraries more recently upgraded saw greater usage growth than those renovations completed earlier in the study period, although 75 percent of the facilities continued to experience higher post-project usage levels. Nearly all of the private institutions (93.1%) experienced usage increases and almost half experienced growth of 100 percent or more. No statistically significant relationship was found between changes in post project usage and: The proportion of facility space allocated for library functions The physical location of the library on campus The size of the library facility The level of degrees offered at the institution The availability of wireless access The number of computers in the instruction lab The number of public access workstations A larger number of seats The number of group study rooms The shelving capacity, the use of compact shelving or off-site storage The presence of coffee or snack bars The presence of any non-library facilities There was a statistically significant correlation (Pearson’s r) between increased post project usage and: The institution type (public or private) (p=.000) The number of data ports in the facility (p=.005) The percent of wired seats (p=.034) Ten elements relating to improved quality emerged as statistically significant in relation to increased usage, although the correlation for quality of artificial lighting was not statistically significant (p=.162 n.s.). The statistically significant correlations (Pearson’s r) between quality and increased usage in order of strength of correlation were: the quality of the instruction lab (p=.000); layout (p=.001); public access workstations (p=.006); natural lighting (p=.007); user workspace (p=.008); telecommunications infrastructure (p=.014); overall ambience (p=.020); collection storage (p=.026); heating, ventilating, and air-conditioning system (p=.026); and service point locations (p=.038). Conclusion – This study confirmed that 80 percent of libraries experience usage increase after a library improvement project. The study revealed those investments that cause increased use, and also found that a number of variables previously predicted to cause usage growth were not significant. The study also found that quality of the improvements, additions, and the building are a significant driver of increased use. The median 37.4 percent increase demonstrates that, contrary to reports in the literature (Shill and Tonner 460), overall library usage is increasing in these institutions.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,052
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,020
Score d'incertitude au seuil0,066

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,052
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,007
Études des sciences et des technologies0,0010,001
Communication savante0,0060,004
Science ouverte0,0010,003
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0200,004

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.

Tête enseignante Opus0,033
Tête enseignante GPT0,354
Écart entre enseignants0,320 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2006
Routes d'admission2
Résumé présentoui

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