Editorial: Innovations in the mental health applications of interRAI assessments
Bibliographic record
Abstract
The sudden uncertainties of the COVID-19 pandemic intensified the existing challenges faced by strained mental healthcare systems worldwide. Betini et al. (2021) assessed the mental health impact of the pandemic on a general population survey in Canada. Using interRAI's self-reported mood scale, the authors gathered data on 3,127 individuals about their mental health in four online surveys spanning April to July 2020. The number of study participants feeling anxious and depressed increased more than 2-fold compared with a prepandemic survey iteration. Nevertheless, the authors emphasise that these sobering statistics are dynamic and can change rapidly in response to social change.Research from Stewart, Vasudeva, et al. (2021) indicates that neither adults nor children were spared from the life-changing consequences of the COVID-19 pandemic. Stewart, Vasudeva, et al. (2021) examined longitudinal routine care data collected from 35,000 children using interRAI 's Child and Youth Mental Health Assessment [ChYMH]. Paradoxically, the researchers found sharp declines in the number of children and youth referred to mental health services during lockdowns. This disparity exposes a need to increase children's access to mental healthcare in times of crisis In a subsequent paper, Stewart and colleagues (2022) highlight the versatility and applicability of interRAI's ChYMH in a fragmented children's mental healthcare system unable to supply the demand for child mental health services. The review elucidates that the lack of coordination between the numerous mental health professionals involved drives inefficiency. The authors state that by taking an integrated approach to assessing a child's strengths, needs, and preferences, interRAI's suite of child and mental health assessment instruments provides an evidence-informed solution to these problems across sectors .A paper by Hirdes and colleagues (2022) focused on the development of new measures of mood disturbance with a sample of about half a million individuals. The contexts ranged from the general community-based population to persons in long-term care and palliative care programs. The paper demonstrates the feasibility of large-scale consistent measurement of mood across populations with differing levels of health, functional ability, and cognition. A remarkable result was that the level of severely distressed mood during the pandemic was seven times greater in the general population compared with a pre-pandemic sample. This level of distress approached what was seen in clinical populations receiving community mental health services.Mental health problems in the workplace are prevalent worldwide, but the needs of injured workers who receive psychiatric services remain elusive (OECD, 2021). A Canadian observational study by Herring et al. (2021) used the richness of data collected between 2006 and 2016 from the interRAI MH instruments to provide a unique insight into the needs of this distinct population. Concerningly, the authors found that workers experienced more trauma, pain, depression, sleeping issues, and substance use disorders than other psychiatric inpatients. Herring et al. (2021) emphasise the importance of ongoing interRAI measurements to capture and respond to the symptoms and needs of a growing patient population. 2021) examined QoL of adults in mental health settings with a 7-nation study including low, mid, and high resource countries using the Self-Reported QoL Survey for Mental Health and Addictions [SQoL-MHA]. Given interRAI's seamless integration of items and scales across all its assessment tools -including those for inpatient and community-based mental health services-the study was able to measure QoL's objective and subjective realms. Participants from Canada and Finland scored particularly high for the hope and activities dimension. On the other hand, patients from Rwanda, Belgium, and Brazil reported good relationships with staff. The findings suggest that strength-based international collaboration could benefit patient's quality of life.Older adults constitute the fasted growing age group, with the estimated number of people aged 65 years and over exceeding 727 Million (United Nations Department of Economic and Social Affairs, 2020). Older adults constitute a vulnerable population with elevated levels of mental health or substance use disorders. Home care has emerged as a viable strategy to reduce hospital or long-term care institutionalisation. Poss and colleagues (2021) examined how many community-dwelling older adults had psychiatric diagnoses and other mental health symptoms and what proportion of these patients visit a psychiatrist. Responses to the interRAI Home Care data showed that only a quarter of participants visited a psychiatrist, despite more than half having psychiatric diagnoses. The authors highlight important questions about differential access to psychiatry services by site of care, geographical location, and age.Once institutionalised, frail older adults become exposed to institution-acquired complications and interventions such as infection, malnutrition, and control interventions with adverse physical and psychological effects. Using routinely collected data from 200,000 interRAI MH assessments, Cheung and colleagues (2021) examined determinants of control interventions (e.g., physical or chemical restraint) in inpatient psychiatry. Their research highlights that people with functional impairment, psychosis, aggressive behaviour, cognitive impairment, and delirium were at risk of controlled interventions in non-emergency situations. Considering that these can have negative health effects, the authors advocate for other strategies to support older adults in these situations.On an optimistic note, research from Howard et al. (2021) shows that long-term care settings provide person-centred care to an increasingly inclusive population of disabled and medically complex persons. Their study analysed longitudinal data from the third-generation interRAI Minimum Data Set to determine if the nursing home transition towards person-centred care continues in today's diverse patient landscape. Although less conducive to social well-being, the authors conclude that person-centred care in US nursing homes provides the necessary foundation to promote mental and physical well-being for persons with complex needs.An emerging body of literature indicates that resilience, the positive mood response observed in response to stress or adversity, promotes well-being in older adults (Angevaare et al., 2020). In a world-first study, Angevaare et al. (2022) used routine care data collected from older Dutch residents of long-term care facilities to explore the mental health effects of different psychological stressors. The interRAI dataset enabled the authors to compare associations between both observer and self-reported mood outcomes. Remarkably, the study found that in their Dutch sample, major life stressors, particularly conflict with other care recipients and staff, were associated with positive mood symptoms.Measuring changes in cognition over time is crucial for the early detection and treatment of cognitive impairment in older adults. The InterRAI Cognitive Performance Scale [CPS]ranging from 0 (intact) to 6 (very severe impairment) -and the Montreal Cognitive Assessment-5 min protocol [MoCA 5-min] -ranging from 0 to 30-are frequently used for measuring cognition in long-term and clinical care settings. Since older adults frequently move between these settings, Andersson et al. (2021) were able to link scores on both instruments to facilitate the tracking of cognition across the continuum of care. The authors found that a CPS score of 0 (intact) and 3 (moderate) corresponds to a MoCA 5-min score of 24 and 0, respectively. This study demonstrates the opportunity of cross-walking scores between the two cognitive measures; however, the authors noted that CPS had higher sensitivity for severe cognitive impairment, whereas the MoCA 5-min was superior for measuring mild impairment.While many articles in this special issue draw on existing data extracted from interRAI assessments, three publications explored the psychometric evaluations of new interRAI mental health instruments. First, Barbaree et al. (2021) describe the development of a forensic supplement to the MH and Problem Behaviour Scale. The instrument underwent rigorous evaluations in three samples of adult forensic inpatients, prison inmates, and youth in custody. The authors state that their innovative tool enables mental health professionals to predict which inpatients are at risk of violence across forensic settings. In practical application, early identification could facilitate appropriate treatments to reduce the need for acute control interventions and to manage behaviours that could hinder the person's progress toward community reintegration. Stewart, Celebre et al. (2022) designed an algorithm to predict violence specifically in children and youth. The authors deemed the interRAI Risk of Injury to Others (RIO) algorithm a strong predictor of violence that can competently assist decision-making and facilitate early intervention. Lastly, Stewart, Celebre, et al. (2021) designed and validated a novel Autism Spectrum Screening Checklist [ASSC]. The authors report that the ASSC can serve as an initial screen to identify high-risk children and youth assessed as part of routine practice.In summary, the articles in this research topic provide valuable insights into the richness of interRAI data and recent mental health innovations within the interRAI suite of assessment systems. These data are not exclusive to interRAI fellows, but researchers interested in exploring further the value of the data are encouraged to contact the authors or visit our website. Moreover, these studies and the new innovations support the needs and outcomes of persons with mental illness across different care settings, age groups, and countries.
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 | 0.015 | 0.098 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.026 | 0.034 |
| Insufficient payload (model declined to judge) | 0.022 | 0.015 |
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".