Bibliographic record
Abstract
Due to medical advances in burn care, the survival rate of individuals with serious burns has significantly increased. This has lead to a great need to focus on psychological aspects of burn injury recovery, particularly how people adapt to their changed bodies. The literature indicates that burn size and severity is not directly associated with the degree of distress and that for women, dissatisfaction with their bodies increases in the year after injury. In this study, women’s experiences of their bodies were investigated by asking them about pain, social relationships, mental health, and appearance. In-depth interviews were conducted with female burn survivors in the first year after injury and the transcripts were analyzed using a narrative-discursive analytic methodology. On the surface, the women told narratives which emphasized how well they were doing, however, further analysis revealed subordinate narratives which indicated body dissatisfaction and difficulties with adjustment. In order to suppress narratives of distress, the women engaged in “self-silencing,” of which three forms are outlined. The self-silencing functioned to help the women resist the cultural devaluing associated with “disfigurement” and more personally, to maintain close relationships. As self-silencing has been linked to depression and anxiety, encouraging women to discuss their difficulties may prove to be pertinent in psychological adjustment following burn injury.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".