Developing a Web Platform to Support a Community of Practice: A Mixed Methods Study in Pediatric Physiotherapy
Bibliographic record
Abstract
INTRODUCTION: Web platforms are increasingly used to support virtual interactions between members of communities of practice (CoP). However, little is known about how to develop these platforms to support the implementation of best practices for health care professionals. The aim of this article is to explore pediatric physiotherapists' (PTs) perspectives regarding the utility and usability of the characteristic of a web platform developed to support virtual communities of practice (vCoP). METHODS: This study adopted an explanatory sequential mixed methods design. A web platform supporting the interactions of vCoP members was developed for PTs working with children with developmental coordination disorder. Specific strategies and features were created to support the effectiveness of the platform across three domains: social, information-quality, and system-quality factors. Quantitative data were collected from a cross-sectional survey (n = 41) after 5 months of access to the web platform. Descriptive statistics were calculated. Qualitative data were also collected from semistructured interviews (n = 9), which were coded, interpreted, and analyzed by using Boucher's Web Ergonomics Conceptual Framework. RESULTS: The utility of web platform characteristics targeting the three key domain factors were generally perceived positively by PTs. However, web platform usability issues were noted by PTs, including problems with navigation and information retrieval. DISCUSSION: Web platform aiming to support vCoP should be carefully developed to target potential users' needs. Whenever possible, users should co-construct the web platform with vCoP developers. Moreover, each of the developed characteristics (eg, newsletter, search function) should be evaluated in terms of utility and usability for the users.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".