Development of a bilingual interdisciplinary scale assessing self-efficacy for participating in Medical Assistance in Dying
Bibliographic record
Abstract
Medical Assistance in Dying (MAiD) is a complex process involving the person seeking care and their relatives. MAiD involves physical, psychosocial and spiritual needs, and consequently the involvement of an interdisciplinary team is beneficial. Therefore, updating the knowledge and skills of healthcare and social services professionals is critical. An interdisciplinary team from Laval University (Quebec, Canada) has developed a continuous training program for all health care and social services professionals who could be involved in the care of persons who request MAiD and their loved ones. It is crucial to assess whether the objectives of the continuous training program are being met, especially since this new training addresses several complex issues (legal, ethical, and clinical). Bandura's self-efficacy theory has been widely used to develop scales for assessing the impact of training programs and identifying knowledge gaps. Bandura's theory states that feeling secure in one's self-efficacy leads to self-determined motivation. Although there are various scales intended to measure self-efficacy in palliative care, none include self-efficacy for participating in the process surrounding MAiD. As a result, we aim to create a bilingual (English-French) interdisciplinary scale to assess self-efficacy for participating in the process surrounding MAiD. The scale will allow decision-makers and researchers to identify current knowledge gaps. It will also be useful for assessing the impact of current and future training programs addressing this end-of-life practice. In this work in progress, we briefly introduce the training program and the future steps in the development and validation of the scale.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".