Clinical Practice Standards and Ethical Issues Applied to a Virtual Group Intervention for Spousal Caregivers of People with Alzheimer's
Bibliographic record
Abstract
Advances in technology have improved access to health and social services by offering more abundant and convenient choices for clients. In particular, the use of technology for delivering services to older adults and their families offers new possibilities for service delivery, by reaching people who are often isolated, and have difficulty accessing traditional services. Despite the continued advances in technology development and its integration into healthcare delivery, health care practitioners need to consider how to adapt and uphold clinical practice standards and address ethical issues in an e-health environment. Given the gap in the literature with respect to discussing these issues, this paper illustrates relevant issues in the context of developing and evaluating an Internet-based intervention for spousal caregivers of persons with dementia. Based on a four year project, a psychotherapeutic group intervention was delivered via the Internet to three groups of spousal caregivers. This article identifies some of the key practice standards and ethical issues that arise when using computer technology to deliver a psychotherapeutic group intervention. The article will also provide examples of relevant issues related to maintaining practice standards and ethical procedures that need to be addressed during the application of a computer-based psychotherapeutic group intervention for spousal caregivers of people with dementia.
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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 | 0.467 | 0.598 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.027 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.019 | 0.016 |
| 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".