Consumer engagement in the orbit (online, recovery-focused, bipolar individual therapy) project
Bibliographic record
Abstract
Background: The ORBIT project is a comparative effectiveness trial of two online interventions, both designed to improve quality of life in late stage BD (operationalized as 10 or more episodes of any type). The aim of this talk is to review learnings from the various ways in which consumers have been engaged with the project thus far.\nMethods: Consumers have been involved in conceptualization the project’s aims, piloting the intervention, refining intervention content (consumer interviews are used to deliver content), user- \ninterface decisions, ethical framework and strategies, intervention delivery, trial recruitment and publicity. We have involved consumers through our long-standing community-based participatory research structures in Canada’s CREST.BD network, the UK Spectrum group, and a developing consumer network in Australia.\nResults: Benefits and key challenges of consumer engagement across various phases of research will be discussed. For example, we will discuss how engagement with the interventions is maximized by creation of a community of participants in the online environment (using discussion boards, posted public responses to videos), and our approach to training consumers who monitor discussion boards and respond constructively to participants’ posts. Reflecting the notion that researchers and consumers have complementary expertises, some aspects of the project have had little or no consumer involvement—choice of measures, statistical analysis plan, project management and budgeting.\nConclusions: The ORBIT project exemplifies how consumer involvement can enrich psychological interventions for BD, and also the scientific framework in which interventions are tested.
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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 | 0.014 | 0.015 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".