Improving geriatric mental health nursing care: Making a case for going beyond psychotropic medications
Bibliographic record
Abstract
Providing high-quality mental health nursing care should be an important and continuous preoccupation in the gerontological nursing field. As the proportion of elderly people in our society is growing, the emphasis on high-quality care will receive increasing attention from administrators, politicians, organized groups, researchers and clinical nurses. Recent findings illustrate unequivocally the important contribution of nurses to achieving the goal of high-quality geriatric care. However, the quality of care for the elderly with psychological difficulties has not been addressed. The objective of this article is to illustrate that while nurses can accomplish much to improve the well-being and mental health of the elderly, their skills are often underutilized. Psychotropic drugs are often the first-line interventions used by health-care professionals to treat mental health concerns of elderly persons. Alternative therapies that could be implemented and evaluated, such as psychological counselling, supportive counselling, education and life review, are infrequently used. Nevertheless, current scientific data suggest that it would be very advantageous if nurses were to play a dominant role in the care of elderly people who are depressed or experiencing sleep pattern disturbances. The same can be said about elderly chronic users of benzodiazepines, as well as those with cognitive impairment. Evidence for the use of psychotropic medications as a viable treatment option for the elderly both in the community and in the long-term care setting who are experiencing mental health challenges is examined. Alternative non-pharmacological approaches that nurses can use to augment care are also briefly discussed.
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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 | 0.025 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.012 | 0.028 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.040 | 0.045 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".