Systemic perspective of violence and aggression in mental health care: Towards a more comprehensive understanding and conceptualization: Part 2
Bibliographic record
Abstract
This is the second of a two part paper which seeks to explore a wide range of phenomena that have been found to have an association with aggression and violence (A/V) in inpatient mental health care, synthesize these propositions according to fit or congruence into a systemic model of A/V, explore the empirical evidence pertaining to these propositions, and begin to consider application of this model to better inform our individual and/or organizational responses to A/V in mental health care. The systemic model is comprised of four thematic categories with part two of the paper focusing on the final two categories: mental health-care system-related phenomena and clinician-related phenomena. The paper then discusses a number of implications arising out of embracing a more systemic model of A/V in mental health care. In broadening our understanding to include all the phenomena that contribute increased risk of A/V incidents, we are able to move away from inaccurate views that disproportionately assign 'responsibility' to clients for causing A/V when the evidence indicates that the client-related phenomena may only account for a small portion of these incidents.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".