Re: Teenage decision-making in the context of the Jehovah's Witness faith (again)
Bibliographic record
Abstract
The author responds; Assessing decision-making capacity is context dependent. It involves determining the person’s ability to understand their health condition, current health problems, the treatment that is being proposed and its potential harms and benefits, other treatment alternatives and potential harms and benefits, and potential consequences of accepting or refusing treatment. It also involves determining that the person is able to contextualize this information to his or her own circumstances. As part of the assessment process, the health care provider doing the assessment should provide the person with the information about the matters detailed in the previous paragraph, and ensure that he or she appreciates that there are options. If he or she is not able to understand the information and appreciate that there are options, then the health care provider would not likely deem him or her capable to give or withhold consent. I concluded in my article that “If the patient is capable, and freely choosing to forgo treatment, you should honour his or her wishes, or transfer care to someone who will.” Because having decision-making capacity entails being fully informed and having the ability to understand the information that has been provided, and appreciating that one has options, when a person is found to be both capable and making a free choice, I would be interested in the ethical argument that would justify overriding this person’s wishes.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".