Using Routinely Collected Clinical Assessments in Mental Health Services: The Resident Assessment Instrument—Mental Health
Notice bibliographique
Résumé
Dear Editor: Dr Urbanoski and colleagues1 examined the use of the Resident Assessment Instrument–Mental Health (RAI-MH) for specialized inpatient mental health services. While the article underscores the importance of a comprehensive approach to implementation (for example, training or information technology infrastructure), much of the critique appears to reflect a lack of understanding of the design and applications of the RAI-MH. Urbanoski et al imply that the RAI-MH system was developed outside of real-world contexts when, in fact, front-line clinicians were engaged in all aspects of the development and refinement of the instrument and its applications. Numerous studies since the development work were based on data collected within routine clinical practice, including research on the Cognitive Performance Scale,2 Clinical Assessment Protocols,3,4 and quality indicators.5 The suggestion that most RAI-MH scales are “irrelevant for most patients”1, p 692 is particularly surprising and misguided. The authors incorrectly identified several scales as outcome measures, or had flawed operationalizations of specific scales. For example, the embedded CAGE (Cut down, Annoyed, Guilty, and Eye-opener) index was evaluated as an outcome measure when it was intended only to be used as a screener for substance abuse. The authors failed to consider the 90-day, look-back period for the RAI-MH items used to populate the CAGE (that is, there may have been overlap between time 1 and 2 observations). Further, conclusions that the RAI-MH lacks indicators of addiction severity are misleading, given that it includes numerous items related to substance and alcohol use, gambling, mental state, involvement with the criminal justice system, and vocational and interpersonal functioning. These measures provide ample opportunity to derive meaningful indices of addiction severity. Urbanoski et al1 also appear to have incorrectly calculated scale values in their study, which makes their conclusions about the use of these scales among specialized populations questionable. A range of 0 to 8 was reported for the Positive Symptom Scale (PSS), though this scale should range from 0 to 12. We analyzed RAI-MH data provided by the Canadian Institute for Health Information for 276 055 people with and without schizophrenia in 75 hospitals across Ontario between 2005 and 2012. The mean PSS was 1.20 (SD 2.22) for people without schizophrenia, and 4.15 (SD 3.25) among people with schizophrenia or other psychotic disorders. For people with schizophrenia, an effect size of 1.32 was found for change in the PSS between admission and discharge assessments. These findings provide clear evidence in support of the PSS. Urbanoski et al1 conclude that the difficulties experienced by a single organization’s implementation of an assessment system cannot be attributed to “either to the assessment platform or to issues of staff motivation and compliance.”1, p 693 Real-world evidence from 74 other hospitals would appear to contradict Urbanoski et al’s report. It is concerning that staff interviewed in this study identified little value in an assessment that includes items paramount to mental health care, including harm to self and others, social and vocational functioning, and traumatic life events, among others previously mentioned. Perhaps the implementation of innovative decision support applications for the RAI-MH in shared clinical decision-making contexts may enhance applications of this system.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,024 | 0,182 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,014 | 0,018 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».