Training may affect primary care staff access to the biomedical electronic evidence base
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.191 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".