Evaluating a knowledge exchange intervention in cancer survivorship care: a workshop to foster implementation of Online Support Groups
Bibliographic record
Abstract
PURPOSE: The purpose of the research described here is to assess the overall effectiveness of the workshop format as a Knowledge Exchange (KE) strategy in (1) disseminating scientific evidence, clinical experience, and systems information related to professionally led Online Support Groups (OSG) for cancer survivors and (2) facilitating the implementation of this intervention by a select group of end users--decision makers and clinical leads in psychosocial supportive care. METHODS: The KE-Decision Support (KE-DS) Model, operationalizing the Health Technology Approach, guided the development of pre- and postworkshop questionnaires, and a follow-up questionnaire administered 5 months after the workshop. Questionnaire results were categorized according to participants' responses to these elements: methods of engagement, evidence (scientific, experiential, systems) and the delivery of this evidence, and external factors at the institutional level, such as administrative support, budgetary issues, etc., that influence decision-maker abilities and strategies. RESULTS: Traditional KE strategies such as peer-reviewed journal articles are optimal for disseminating scientific evidence, while face-to-face interactions, such as in a workshop, are best used to disseminate systems-level implementation information, such as fiscal implications, budgetary requirements, and policy relevance, which is not found in journal articles or conferences. An apparent shift in workplace culture signifies the availability of institutional support for high-level staff to engage in KE. CONCLUSIONS: As a KE strategy with identified end users, the workshop format is effective in facilitating the implementation of this intervention in participants' institutions.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".