Training may affect primary care staff access to the biomedical electronic evidence base
Notice bibliographique
Résumé
A review of: Doney, Liz, Helen Barlow, and Joe West. “Use of Libraries and Electronic Information Resources by Primary Care Staff: Outcomes from a Survey.” Health Information and Libraries Journal 22.3 (September 2005): 182-188. Objective – To assess use of existing local libraries, the Internet, and biomedical databases by primary care staff prior to implementation of the Primary Care Knowledge Management Projects. Additionally, to assess the need to train primary care staff to use the Internet and biomedical databases. Design – Cross-sectional postal questionnaire survey. Setting – Nottingham and Rotherham, two cities in the Trent region of the UK. Subjects – Questionnaires were analyzed from 243 general practitioners, practice nurses, and practice managers in four Nottingham primary care trusts as well as practices in the Rotherham Health Authority area. Methods – Questionnaires and cover letters were sent between May 2001 and February 2002. To encourage response, a postage-paid envelope was enclosed. A total of 709 questionnaires were sent in Nottingham, and 169 were returned for a response rate of 24%. In Rotherham, 179 questionnaires were sent and 61 returned, for a 34% response rate. Thirteen responses from a May 2001 pilot in Rotherham were also included in the data analysis. Survey questions included a variety of formats, including tick boxes and open-ended questions. Data was entered into an Access database and analysis was performed using Stata software. Main results – Reported use of libraries was low overall, with only 30% of respondents claiming to have used library facilities. However, there was significant variation among professional groups. Practice nurses (PNs) had significantly higher usage of libraries than general practitioners (GPs) and practice managers (P < 0.01). Overall, 81% of the respondents used the Internet for work, with no significant variation by group. Forty-four percent reported using biomedical databases, with some significant variation. GPs and PNs reported higher usage of databases than practice managers (P < 0.01). The most common reported barrier to using both the Internet and biomedical databases was lack of training. GPs more frequently cited lack of time as a barrier to using biomedical databases (P = 0.04). Over half of all respondents reported an interest in Internet training, and over 60% reported an interest in database search training. A significantly lower number of practice managers wanted database training (P = 0.02). Conclusion – Based on the results of this admittedly small study, additional training is needed – and desired – by primary care staff in both Nottingham and Rotherham. Developing and offering training in Internet searching and evaluation as well as use of the biomedical databases is one important way in which libraries can build partnerships with primary care practitioners. This will also enable added numbers of primary care staff to access and use the clinical evidence knowledge base. Additional studies are needed to identify and overcome barriers to training.
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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 | 0,022 | 0,191 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,025 | 0,002 |
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 ».