“What Bothers Me Most Is the Disparity between the Choices that People Have or Don’t Have”: A Qualitative Study on the Health Systems Responsiveness to Implementing the Assisted Decision-Making (Capacity) Act in Ireland
Bibliographic record
Abstract
Objective: The Assisted Decision-Making (ADM) (Capacity) Act was enacted in 2015 in Ireland and will be commenced in 2021. This paper is focused on this pre-implementation stage within the acute setting and uses a health systems responsiveness framework. Methods: We conducted face-to-face interviews using a critical incident technique. We interviewed older people including those with a diagnosis of dementia (n = 8), family carers (n = 5) and health and social care professionals (HSCPs) working in the acute setting (n = 26). Results: The interviewees reflected upon a healthcare system that is currently under significant pressures. HSCPs are doing their best, but they are often halted from delivering on the will and preference of their patients. Many older people and family carers feel that they must be very assertive to have their preferences considered. All expressed concern about the strain on the healthcare system. There are significant environmental barriers that are hindering ADM practice. Conclusions: The commencement of ADM provides an opportunity to redefine the provision, practices, and priorities of healthcare in Ireland to enable improved patient-centred care. To facilitate implementation of ADM, it is therefore critical to identify and provide adequate resources and work towards solutions to ensure a seamless commencement of the legislation.
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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 | 0.032 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".