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Enregistrement W4388715217

Point-of-Care Healthcare Databases Are an Overall Asset to Clinicians, but Different Databases May Vary in Usefulness Based on Personal Preferences. A Review of: Chan, R. & Stieda, V. (2011). Evaluation of three point-of-care healthcare databases: BMJ Point-of-Care, Clin-eguide and Nursing Reference Centre. Health and Information Libraries Journal, 28(1), 50-58. doi: 10.1111/j.1471-1842.2010.00920.x

2011· review· en· W4388715217 sur OpenAlexaboutno aff
Carol D. Howe

Notice bibliographique

RevueDOAJ (DOAJ: Directory of Open Access Journals) · 2011
Typereview
Langueen
DomaineHealth Professions
ThématiqueElectronic Health Records Systems
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDatabaseAsset (computer security)Health carePoint (geometry)Computer sciencePolitical scienceMathematics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Objective – To evaluate the usefulness of three point-of-care healthcare databases (BMJ Point-of-Care, Clin-eguide, and Nursing Reference Centre) in clinical practice. Design – A descriptive study analyzing questionnaire results. Setting – Hospitals within Alberta, Canada’s two largest health regions (at the time of this study), with a third health region submitting a small number of responses. Subjects – A total of 46 Alberta hospital personnel answered the questionnaire, including 19 clinicians, 7 administrators, 6 nurses, 1 librarian, 1 preceptor, and “some” project coordinators. Subjects were chosen using a non-probability sampling method. Methods – The researchers developed an online questionnaire consisting of 17 questions and posted it on the University of Calgary’s Health Sciences Library and the Health Knowledge Network websites. The questions, in general, asked respondents how easy the databases were to search and use, whether the database content answered their clinical questions, and whether they would recommend the databases for future purchase. Most questions required a response for each of the three databases. The researchers collected quantitative data by using a Likert scale from 1 to 5, with 5 being the most positive answer and 1 being the most negative. They collected qualitative data by asking open-ended questions. Main Results – With regard to ease of searching, BMJ Point-of-Care (BMJ) received the greatest number of responses (71%) at level 5. A smaller number of respondents (56%) rated Nursing Reference Centre (NRC) at level 5. Clin-eguide received 59% of the responses at level 5, but it also received the greatest number of responses at the next highest level (level 4). Respondents rated all three databases similarly with regard to levels 1 and 2.Regarding how easy the resources were to learn, most respondents rated all three databases as easy to learn (BMJ, 77%; Clin-eguide, 72%; and NRC, 68%). Very few respondents thought any of the databases were difficult to learn.The researchers gleaned from open-ended questions that the respondents generally thought all three databases were faster and easier to use than the conventional databases they had used. Respondents did not always agree with one another, however, about which features they liked or why.With regard to content, most respondents agreed that the information in all three databases was relevant to their needs (94.6% for Clin-eguide and 87.9% for BMJ and NRC). Respondents also generally agreed that all three databases answered their questions to a high degree. Clin-eguide had the highest percentage of answers at levels 4 and 5 and the lowest percentage of answers at level 2. NRC was the reverse, with the lowest percentage of answers at levels 4 and 5 and the highest percentage of answers at level 2. Still, the researchers felt that all three databases answered respondents’ questions to a similar degree. In the open-ended questions, respondents voiced additional likes and dislikes about content, but again, answers among respondents were not consistent with one another.Respondents were asked how often they would use the resource if it were available though their library. The majority of BMJ users reported that they would use it extensively or moderately. About 36% and 39% of NRC users reported they would use it extensively or moderately, respectively; while 43.5% and 34.8% of Clin-eguide users reported they would use it extensively or moderately, respectively. When asked if they would recommend the resource for the library, 84.8% would recommend Clin-eguide, 75% would recommend BMJ, and 67.6% would recommend NRC. The open-ended questions generally indicated that respondents would recommend all three databases.Regarding how respondents preferred training on these resources, users preferred online tutorials to learn Clin-eguide and NRC. Users preferred website tips and instruction to learn BMJ. The least preferred methods of training for all three databases were live demonstration and classroom training. Conclusion – None of the databases particularly stood out with regard to usability and content. The respondents generally liked all three databases.It is important to note, however, that detailed comparisons among the databases were difficult to make. First, respondents did not always give an answer for all three databases for a given question. Because of this, and to present a more meaningful analysis, the researchers often reported the number of respondents who answered a certain way as a percentage rather than a number. Second, although the respondents generally liked all three databases, opinions about likes and dislikes were not consistent among respondents. For example, one respondent thought the NRC and Clin-eguide interfaces were more difficult to navigate than BMJ, while another respondent thought BMJ had the harder-to-navigate interface. The researchers felt that respondents’ prior experience with the databases may have influenced their preferences. They were unable to determine if the respondents' professional interests had any influence on their preferences. Inconsistent responses made it difficult for researchers to assign an overall value to a given database. Therefore, this survey did not help to make definitive purchasing decisions. The researchers felt they would have to look at each resource much more carefully to make such a decision.The researchers noted several ideas for future research of this sort. They acknowledged that the sample size was not big enough to determine statistical significance and thought that better marketing of the questionnaire may have increased the numbers. They also thought that it would be interesting to observe the respondents using the databases in real-time to find out such things as: what information they require in their daily work, how long it takes them to find it, and what they do with it once they find it.

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)0,064
score de la tête « metaresearch » (Gemma)0,239
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: aucune
Score de désaccord entre enseignants0,064
Score d'incertitude au seuil0,340

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0640,239
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0070,014
Études des sciences et des technologies0,0020,002
Communication savante0,0110,015
Science ouverte0,0030,007
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0130,005

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 Opus0,627
Tête enseignante GPT0,626
Écart entre enseignants0,002 · 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'étudeSans objet
Domainenon disponible
GenreSynthèse

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

En bref

Citations0
Publié2011
Routes d'admission1
Résumé présentoui

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