Social work practice: A look at competency assessments with older adults in healthcare settings
Bibliographic record
Abstract
This research contributes to the social work understanding of mental competency assessments with older adults in healthcare settings. Utilizing a narrative research methodology, this qualitative research study analysed nine face-to-face interviews with social workers with experience assessing competency of older adults in the following Winnipeg, Manitoba healthcare settings: hospitals, personal care homes, and a number of community settings (home care, geriatric specialty programs, and private practice). Drawing from systems and ecological theories, as well as the social determinants of health, the results of this study revealed several key concerns such as the motivation behind what triggers an assessment, the specific tests and methods used to determine competency, inequitable treatment of the patient throughout the assessment depending on their cultural or socioeconomic background, and depending on the setting whether the social worker felt their role on the assessment team was valued or dismissed. Recommendations outlined implications for: enhancing the quality of the competency assessment process; expanding the role of social work in interdisciplinary settings; examining the use of methods and tests for assessment; and exploring opportunities for change in legislation, education and early detection. Potential areas of further study are 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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".