Balancing competing interests and obligations in mental health‐care practice and policy
Bibliographic record
Abstract
It is often challenging for mental health-care providers and health organizations to perform their various roles and to meet their varied obligations. In complex mental health-care circumstances the concurrent application of relevant ethical principles and values often leads to the emergence of completing obligations that need to be carefully weighed and balanced in the making of care-related decisions. Although some clinical circumstances, such as those potentially triggering the duty to warn, are adequately guided by existing rules based on legal precedents, there is a gap in decision-making support in other mental health-care domains. This article proposes that a set of targeted, decision-making approaches be developed to assist in the handling of specific, challenging circumstances. By way of illustration, two novel approaches are introduced; that is, choosing to work within a moral relational space of optimal therapeutic engagement (at the micro level of clinical practice), and the use of a health policy development approach that instantiates deliberative engagement (at the meso and macro levels of health organization).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.162 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.105 |
| Scholarly communication | 0.032 | 0.019 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.029 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".