Language and pain predict persistent depression among seriously ill home care clients
Bibliographic record
Abstract
Abstract Objectives This study examined potential predictors of persistent depressive symptoms in a cohort of seriously ill older adults (aged 65+ years) receiving home care services. Methods This was a retrospective cohort study using secondary data collected from the Resident Assessment Instrument for Home Care for all assessments completed between 2001 and 2020. The cohort included seriously ill individuals with depressive symptoms at baseline and who continued to have depressive symptoms on reassessment within 12 months (n = 8,304). Serious illness was defined as having severe health instability, a prognosis of less than 6 months, or a goal of care related to palliative care (PC) on admission to the home care program. Results The mean age of the sample was 80.8 years (standard deviation [SD] = 7.7), 61.1% were female, and 82.1% spoke English as their primary language. The average length of time between assessments was 4.9 months (SD = 3.3). During that time, 64% of clients had persistent symptoms of depression. A multivariate logistic regression model found that language, pain, caregiver burden, and cognitive impairment were the most significant predictors of experiencing persistent depressive symptoms. Significance of results Persistent depressive symptoms are highly prevalent in this population and, left untreated, could contribute to the person experiencing a “bad death.” Some of the risk factors for this outcome are amenable to change, making it important to continually assess and flag these factors so interventions can be implemented to optimize the person’s quality of life for as long as possible.
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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".