Development and usability of a feedback tool, “My Personal Brain Health Dashboard”, to improve setting of self-management goals among people living with HIV in Canada
Bibliographic record
Abstract
OBJECTIVE: (1) To develop a personalized health outcome profile as a feedback tool to improve self-management in people living with chronic conditions such as HIV and (2) to evaluate the interpretability and usefulness of the feedback tool for setting specific goals. METHODS: The development of "My Personal Brain Health Dashboard" was inspired by the knowledge-to-action framework. A health outcome profile was computer generated in SAS from the outcome measures, at first and last recorded visits, of each person enrolled in the +BHN cohort from five sites in Canada. The Wilson-Cleary model framed the outcome measurement strategy. Single actionable items with evidence of life impact were chosen. The response option from the original item was the person's value and the optimal level was provided to help persons compare their results to an optimal target. Cognitive interviews were conducted with members of HIV community. Appropriateness of the Dashboard for goal-setting was tested by asking participants to write specific goals according to the Dashboard they were given. RESULTS: Fifteen respondents were recruited from Montreal and Vancouver. Items most endorsed to be changed were cognition, pain, and body mass index. 80% found the Dashboard useful for setting health-related goals. A total of 85 goals were set, the text of which was mined to create a lexicon for scoring goal quality in future endeavours. CONCLUSION: This study was the preparatory phase for a future trial on a method to stimulate setting specific goals. The future trial would provide a thorough understanding of the quality of person-defined goals.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".