A New Tool for Detecting COVID-19 Psychological Burden Among Postacute and Long-term Care Residents (Mood-5 Scale): Observational Study
Bibliographic record
Abstract
BACKGROUND: Older adults are at high risk for developing serious somatic and psychological symptoms associated with COVID-19. Currently available instruments may not be sensitive to the concerns about COVID-19 in postacute and long-term care and their applications in telehealth remain to be clarified. OBJECTIVE: We investigated the psychometric properties of the Mood-5 Scale (M5) as a rapid self-assessment of the COVID-19 psychological burden among postacute and long-term care residents. METHODS: Residents (N=131), aged 50 years and above, from 20 postacute and long-term care facilities in Maryland, USA, were evaluated in-person or via telehealth (43/131, 32.8%) across a 4-week period (May 11 to June 5, 2020) during the COVID-19 pandemic. The COVID-19 psychological burden experienced by the residents was rated by geriatric psychologists who independently reviewed their clinical documentation. Psychometric analyses were performed on the M5 in relation to psychological tests, COVID-19 psychological burden, and diagnostic data collected during the evaluation. RESULTS: The M5 demonstrated acceptable internal consistency (Cronbach α=.77). M5 scores were not confounded by demographic variables or telehealth administration (P>.08). Convergent validity for the M5 was established via positive associations with anxiety (r=0.56, P<.001) and depressive (r=0.49, P<.001) symptoms. An M5 cutoff score of 3 demonstrated strong sensitivity (0.92) and adequate specificity (0.75) for identifying COVID-19 psychological distress among postacute and long-term care residents (area under the curve of 0.89, positive predictive value=0.79, negative predictive value=0.91). CONCLUSIONS: The M5 is a reliable and valid tool for self-assessment of mood that can help identify postacute and long-term care residents with significant psychological burden associated with COVID-19. It can be completed in less than 1 minute and is appropriate for use in both in-person and virtual visits.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".