Needs assessment : identifying the barriers to admission and supports in long-term care facilities for the mentally ill elderly in the region of London-Middlesex, Ontario / by Jennifer Speziale.
Bibliographic record
Abstract
There are increasing demands for long-term care (LTC) homes to admit elderly \npersons with mental health illness who are unable to be cared for within the community. \nThis project examined the barriers to admission to LTC homes and supports required in \nthese homes for the elderly who experience mental health illness. No longer is LTC \nsimply for the frail elderly population, but current trends now include a younger \npopulation with mental health needs, an increased male population, residents with \ndementia and difficult-to-manage behaviours, developmentally challenged residents, and \nother residents with a variety of mental health diagnosis that may also include concurrent \nsubstance abuse and medical issues. These facilities face unique challenges when \nproviding care for the geriatric mentally ill population not only because of these trends \nbut also because of issues with staffing recruitment and retention; a need for \nappropriately trained staff in mental health; and unmet interdisciplinary staffing needs, \ninadequate psychiatric supports, environmental needs, and fiscal constraints. \nOlder adults who suffer with mental health illness are at an increased risk for \nexperiencing the inequalities within our health care system. This is a very vulnerable \npopulation because their medical and psychological needs related to aging are more \ncomplex than those of the youth and adult populations. Older adults with serious mental \nillness face discrimination and stigma both for their mental health disorders and for their \nage. These facts, in turn, are just some of the barriers facing admission to LTC homes for \nthis population. The increasing senior population, especially in the 85 and older age \ncategory, gives precedence to the urgency of assessing the community resources available \nto meet their needs. The statistics show an alarming incidence of mental health illness \nwithin the geriatric population, with the frequency being as high as one in five being affected over the age of 65 (Jeste et al., 1999, as cited in Bartels, Dums, et al., 2002). It is \nanticipated that the number of people over the age of 65 who will suffer with a mental \nhealth illness will ?more than double by the year 2030, from 7 million in 2000 to 15 \nmillion? (Jeste et al., as cited in Bartels, Dums, et al.).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".