Obtaining Informed Consent from Continuing Care Residents: Issues and Recommendations
Bibliographic record
Abstract
As the number of older adults residing in continuing care facilities increases, mental health professionals will provide more services and conduct more research in this setting. Mental health professionals working with continuing care residents will find themselves regularly challenged by ethical issues, particularly obtaining informed consent. Characteristics of the continuing care setting and residents make obtaining informed consent especially challenging. Mental health professionals must overcome these challenges in order to fulfill the following three requirements of informed consent: (1) the client is competent, (2) the client is provided with sufficient information, and (3) the client has not been coerced and/or the consent is voluntary. This article will examine the issues surrounding the fulfillment of these requirements in a continuing care facility, and will provide suggestions and guidelines that mental health professionals can utilize during the informed consent process.
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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 | 0.396 | 0.612 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.013 | 0.036 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.061 | 0.032 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".