Evaluating the Development of Virtual Communities of Practice that Support Evidence Based Practice
Bibliographic record
Abstract
Objective – The aim of this paper is to examine how virtual community of practice principles might be used by information professionals with emphasis on the work of the Specialist Libraries for health professionals in England, UK. An evaluation conducted in 2004-2005 examined the operation of the Specialist Libraries, which the National Library for Health had contracted out to various organisations, and assessed their stage of development as communities of practice. 
 
 Methods – Evaluation methods included observation of a meeting of information specialists, interviews with clinical leads and information specialists, and evaluation of the content and format of the Specialist Library websites. The evaluation framework was based on a systematic review of the literature to determine the critical success factors for communities of practice and their role in supporting evidence based practice. An updated literature review was conducted for this paper.
 
 Results – Operational structures varied but were mostly effective in producing communities of practice that were at an “engaged” stage. Some Specialist Libraries wished to move towards the “active” stage by supporting online discussion forums, or by providing question and answering services or more learning activities and materials. Although the evidence from the literature suggests there are few clear criteria for judging the effectiveness of communities of practice, the evaluation framework used here was successful in identifying the state of progress and how information professionals might approach designing virtual communities of practice.
 
 Conclusions – Structuring library and information services around community of practice principles is effective. Careful and participative design of the information architecture is required for good support for evidence based practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.201 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".