Exploring caregivers’ knowledge of and receptivity toward novel diagnostic tests and treatments for persons with post-traumatic disorders of consciousness
Bibliographic record
Abstract
BACKGROUND: A paucity of information is available regarding how caregivers of persons with post-traumatic disorders of consciousness (DOC) approach medical decision-making. Yet for evidence-based standards of care to be established, the onus is on caregivers' willingness to enroll their family members in clinical trials of novel tests and treatments (NTT). OBJECTIVE: To gather information regarding the beliefs and opinions of caregivers regarding NTT for DOC. METHODS: Exploratory qualitative data via focus groups from N = 17 caregivers of persons in post-traumatic DOC at both the acute (N = 7) and subacute (N = 10) phases of injury recovery. Supplemental survey data about knowledge of DOC. RESULTS: While attitudes toward NTT were generally favorable, two main themes emerged that influenced willingness to pursue NTT: patient and caregiver-specific factors, and the acquisition/use of information to guide decision-making. While survey data suggested a lack of knowledge about NTT, qualitative data revealed that this was better explained by different standards for knowledge, i.e., anecdotal versus empirical information. CONCLUSIONS: Current findings could support discussion between healthcare providers and caregivers regarding medical decision-making as well as suggestions for how to increase the likelihood of caregivers being willing to enroll their family members in clinical trials of NTT.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".