Bipolar disorder research 2.0: Web technologies for research capacity and knowledge translation
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Current Web technologies offer bipolar disorder (BD) researchers many untapped opportunities for conducting research and for promoting knowledge exchange. In the present paper, we document our experiences with a variety of Web 2.0 technologies in the context of an international BD research network: The Collaborative RESearch Team to Study psychosocial issues in BD (CREST.BD). METHODS: Three technologies were used as tools for enabling research within CREST.BD and for encouraging the dissemination of the results of our research: (1) the crestbd.ca website, (2) social networking tools (ie, Facebook, Twitter), and (3) several sorts of file sharing (ie YouTube, FileShare). For each Web technology, we collected quantitative assessments of their effectiveness (in reach, exposure, and engagement) over a 6-year timeframe (2010-2016). RESULTS: In general, many of our strategies were deemed successful for promoting knowledge exchange and other network goals. We discuss how we applied our Web analytics to inform adaptations and refinements of our Web 2.0 platforms to maximise knowledge exchange with people with BD, their supporters, and health care providers. CONCLUSIONS: We conclude with some general recommendations for other mental health researchers and research networks interested in pursuing Web 2.0 strategies.
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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 | 0.087 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".