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Enregistrement W2101489743 · doi:10.18438/b8xs6b

Statistical Measures Alone Cannot Determine Which Database (BNI, CINAHL, MEDLINE, or EMBASE) Is the Most Useful for Searching Undergraduate Nursing Topics

2011· article· en· W2101489743 sur OpenAlexaffvenue
Giovanna Badia

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2011
Typearticle
Langueen
DomaineNursing
ThématiqueNursing Diagnosis and Documentation
Établissements canadiensMcGill University Health Centre
Organismes subventionnairesnon disponible
Mots-clésCINAHLMEDLINEMedicineNursingPolitical sciencePsychological intervention

Résumé

récupéré en direct d'OpenAlex

A Review of: Stokes, P., Foster, A., & Urquhart, C. (2009). Beyond relevance and recall: Testing new user-centred measures of database performance. Health Information and Libraries Journal, 26(3), 220-231. Objective – The research project sought to determine which of four databases was the most useful for searching undergraduate nursing topics. Design – Comparative database evaluation. Setting – Nursing and midwifery students at Homerton School of Health Studies (now part of Anglia Ruskin University), Cambridge, United Kingdom, in 2005-2006. Subjects – The subjects were four databases: British Nursing Index (BNI), CINAHL, MEDLINE, and EMBASE). Methods – This was a comparative study using title searches to compare BNI (British Nursing Index), CINAHL, MEDLINE and EMBASE. According to the authors, this is the first study to compare BNI with other databases. BNI is a database produced by British libraries that indexes the nursing and midwifery literature. It covers over 240 British journals, and includes references to articles from health sciences journals that are relevant to nurses and midwives (British Nursing Index, n.d.). The researchers performed keyword searches in the title field of the four databases for the dissertation topics of nine nursing and midwifery students enrolled in undergraduate dissertation modules. The list of titles of journals articles on their topics were given to the students and they were asked to judge the relevancy of the citations. The title searches were evaluated in each of the databases using the following criteria: • precision (the number of relevant results obtained in the database for a search topic, divided by the total number of results obtained in the database search); • recall (the number of relevant results obtained in the database for a search topic, divided by the total number of relevant results obtained on that topic from all four database searches); • novelty (the number of relevant results that were unique in the database search, which was calculated as a percentage of the total number of relevant results found in the database); • originality (the number of unique relevant results obtained in the database for a search topic, which was calculated as a percentage of the total number of unique results found in all four database searches); • availability (the number of relevant full text articles obtained from the database search results, which was calculated as a percentage of the total number of relevant results found in the database); • retrievability (the number of relevant full text articles obtained from the database search results, which was calculated as a percentage of the total number of relevant full text articles found from all four database searches); • effectiveness (the probable odds that a database will obtain relevant search results); • efficiency (the probable odds that a database will obtain both unique and relevant search results); and • accessibility (the probable odds that the full text of the relevant references obtained from the database search are available electronically or in print via the user’s library). Students decided whether the search results were relevant to their topic by using a “yes/no” scale. Only record titles were used to make relevancy judgments. Main Results – Friedman’s Test and odds ratios were used to compare the performance of BNI, CINAHL, MEDLINE, and EMBASE when searching for information about nursing topics. These two statistical measures demonstrated the following: • BNI had the best average score for the precision, availability, effectiveness, and accessibility of search results; • CINAHL scored the highest for the novelty, retrievability, and efficiency of results, and ranked second place for all the other criteria; • MEDLINE excelled in the areas of recall and originality, and ranked second place for novelty and retrievability; and • EMBASE did not obtain the highest, or second highest score, for any of the criteria. Conclusion – According to the authors, these results suggest that none of the databases studied can be considered the most useful for searching undergraduate nursing topics. CINAHL and MEDLINE emerge as consistently good performers, but both databases are needed to find relevant material on a topic. Friedman’s Test clearly differentiated between the databases for the accessibility of search results. Odds ratio testing may assist librarians to make decisions about database purchases. BNI scored the highest for availability of results and CINAHL ranked the highest for retrievability. Statistical measures need to be supplemented with qualitative data about user preferences in order to determine which database is the most useful to our users.

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)0,408
score de la tête « metaresearch » (Gemma)0,755
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesMétarecherche
DomaineSignal candidat: Méthodes · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,592
Score d'incertitude au seuil0,730

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,4080,755
Méta-épidémiologie (sens strict)0,0030,002
Méta-épidémiologie (sens large)0,0090,010
Bibliométrie0,0340,058
Études des sciences et des technologies0,0030,006
Communication savante0,0160,025
Science ouverte0,0060,007
Intégrité de la recherche0,0040,004
Charge utile insuffisante (le modèle a refusé de juger)0,0070,003

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 Opus0,061
Tête enseignante GPT0,337
Écart entre enseignants0,276 · 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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
DomaineMéthodes
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

Citations2
Publié2011
Routes d'admission2
Résumé présentoui

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