Bibliographic record
Abstract
Background Home support workers help seniors with daily living tasks in their homes. Many home care clients have both physical and mental health problems. They may have symptoms such as sadness, worry, and confusion. In addition, they may be isolated and lonely. The training that home support workers receive usually focuses on physical care. There is often little emphasis on helping clients with mental health problems. Alberta’s Continuing Care Strategy: Aging in the Right Place recognizes that seniors want to remain in their own homes (Alberta Health & Wellness, 2008). Yet, seniors may have difficulty continuing to live independently. For example, they might be physically weak, and therefore be unable to complete daily tasks like shopping and cleaning. Physical illnesses often occur along with mental health problems such as depression or anxiety. One study conducted in Michigan estimated that 40.5% of seniors that receive support services have a recognizable mental health problem (Li & Conwell, 2007). Lonely seniors may cope by using alcohol or other substances, leading to further disability (Zarit S. & Zarit, J, 2007). Early detection of mental health problems can help to prevent this path of decline. Unfortunately, support services tend to focus on activities of daily living, while mental health needs are neglected or misunderstood. There are serious consequences if mental health problems are not detected or treated. These can include unnecessary pain, a decline in overall health, and decreased physical or mental abilities. These symptoms can then increase the risk of admission to nursing homes, which can cause significant suffering for older adults and their family members (Anstey, et al., 2007; Chuan, Kumar, Matthew, Heok, & Pin, 2008; Parmalee, Katz, & Lawton, 1991).
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.448 | 0.183 |
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".