Relevance and Premises of Values-Based Practice for Decision Making in Brain Health
Bibliographic record
Abstract
Brain health is a complex concept, shaped by a plethora of determinants related to physical health, healthy environments, safety and security, learning and social connection, as well as access to quality healthcare services. Decision-making in this complex field is characterized by diverse values, potentially conflicting interests, and asymmetrically influential stakeholders. Values-based practice (VBP) is a toolkit for balancing values in a democratic and inclusive way, so that every stakeholder feels a sense of ownership over the decision made. In VBP, the emphasis is on good process rather than on pre-determined 'correct' outcomes. Based on two case vignettes, we highlight the relevance of the ten principles of VBP for balancing different values to the satisfaction of those directly concerned, in a given decision-making process. In addition, we argue that the successful implementation of VBP in the complex area of brain health, as well as in other fields, is premised on higher order values (meta-values), beyond mutual respect and the legal, regulatory, and bioethical framework. These include mutual regard, reciprocity, autonomy, and an egalitarian attitude towards VBP procedures and involved stakeholders.
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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 | 0.071 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.127 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".