Bibliographic record
Abstract
Accessible summary The study aimed to understand risk assessment and management approaches on a number of mental health units. Mental health professionals have a number of choices to make when considering risk assessment. Participants were mainly using a clinical approach to risk assessment, which is affected by their own skill level. It is important to consider including more structured approaches to risk assessment, as well as education and training, and client participation. Abstract This exploratory and descriptive study took place in one Canadian province. The study aimed to: (1) to identify and describe the nature and extent of current risk assessment and management approaches used in the adult inpatient mental health and forensic units; and (2) to identify good practice and shortfalls in the nature and extent of the approaches currently utilized. Data were collected from 48 participants through nine focus groups. Participants reported that they used a clinical approach to risk assessment. They had also not considered risk assessment and management as a proactive structured process. Education and training was also limited and skills were developed over time through practice. Five keys issues are discussed as important: reliance on clinical judgement alone is not the best choice to make; the need to consider risk as a whole concept; risk management being more reactive than proactive; education and training; and client involvement in risk assessment.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".