Therapeutic privilege: between the ethics of lying and the practice of truth: Figure 1
Bibliographic record
Abstract
The 'right to the truth' involves disclosing all the pertinent facts to a patient so that an informed decision can be made. However, this concept of a 'right to the truth' entails certain ambiguities, especially since it is difficult to apply the concept in medical practice based mainly on current evidence-based data that are probabilistic in nature. Furthermore, in some situations, the doctor is confronted with a moral dilemma, caught between the necessity to inform the patient (principle of autonomy) and the desire to ensure the patient's well-being by minimising suffering (principle of beneficence). To comply with the principle of beneficence as well as the principle of non-maleficence 'to do no harm', the doctor may then feel obliged to turn to 'therapeutic privilege', using lies or deception to preserve the patient's hope, and psychological and moral integrity, as well as his self-image and dignity. There is no easy answer to such a moral dilemma. This article will propose a process that can fit into reflective practice, allowing the doctor to decide if the use of therapeutic privilege is justified when he is faced with these kinds of conflicting circumstances. We will present the conflict arising in practice in the context of the various theoretical orientations in ethics, and then we will suggest an approach for a 'practice of truth'. Last, we will situate this reflective method in the broader clinical context of medical practice viewed as a dialogic process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".