Validating the Mental Health Assessment Protocols (MHAPs) in the Resident Assessment Instrument Mental Health (RAI‐MH)
Bibliographic record
Abstract
Accessible summary The Mental Health Assessment Protocols (MHAPs) embedded in the Resident Assessment Instrument Mental Health assessment instrument are valid measures, though more complex triggering algorithms capable of differentiating individuals based on outcomes could enhance their clinical relevance to care planning. All but one of the 27 MHAPs had sensitivity rates above 80%, and the specificity was over 80% for 74% of the MHAPs. The use of the MHAPs available in compatible instrumentation for community‐based mental health settings would be useful for enhancing continuity of care. Abstract For persons with mental illness and addictions, comprehensive assessment of their strengths, preferences and needs is central to person‐centred care planning. In this study, the validity of the Mental Health Assessment Protocols (MHAPs) embedded in the Resident Assessment Instrument Mental Health instrument (the mandated assessment system for Ontario adult inpatient psychiatry) is examined, and triggering rates are compared in inpatient and community‐based mental health settings. The sample is based on adults admitted to a psychiatric facility ( n = 963) and to community mental health programmes ( n = 1505) participating in the study. An international panel of mental health experts further evaluated study results. Among the 27 MHAPs, all but one had sensitivity rates above 80%, and the specificity was over 80% for 74% of the MHAPs. The expert panel found that the MHAPs worked well and could be used to support mental health care. The present study found that the MHAPs are valid measures, though more complex triggering algorithms capable of differentiating individuals based on outcomes were suggested to enhance their clinical relevance to care planning. Further, the use of compatible instrumentation in community‐based mental health settings was promoted to enhance continuity of care.
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 | 0.118 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".