To see or not to see: a qualitative interview study of patients’ views on their own diagnostic images
Bibliographic record
Abstract
OBJECTIVES: To ascertain what meaning individuals attach to perceiving images of their own interior body and how the images and their meanings affect the clinical consultation. DESIGN: Face-to-face semistructured interviews. PARTICIPANTS: 25 adult patients in southern England who, within the preceding 12 months, had been referred for diagnostic imaging. SETTING: Community. RESULTS: For patients, being shown their own X-rays, MRIs or CT images creates a variety of effects: (1) a sense of better understanding of the diagnosis; (2) validation of their sensory and emotional response to the illness or injury and (3) an alteration to the tenor and nature of the clinical encounter between patient and physician. In addition to meanings attached to these images, patients also impute meaning to the physician's decision not to share an image with them. The desire to see their image was greater in those patients with a skeletal injury; patients are less keen on viewing abdominal or other soft tissue images. CONCLUSIONS: Viewing images of one's interior, invisible body is powerful and resonant in a number of ways. The experience of not seeing, whether through the patient's or the physician's choice, is also fraught with meaning.
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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".