“My body was my temple”: a narrative revealing body image experiences following treatment of a spinal cord injury
Bibliographic record
Abstract
PURPOSE: This narrative explores the lived experience of a young woman, Rebecca, and her transitioned body image after sustaining and being treated for a spinal cord injury. METHOD: Data were collected from a single semi-structured in-depth interview. RESULTS: Rebecca disclosed her transitioned body image experiences after sustaining a spinal cord injury and being treated by medical staff immediately following her injury. Before her injury, she described a holistic body experience and named this experience her "temple". During intensive care in the hospital, she explained her body was treated as an object. The disconnected treatment of her body led to a loss of the private self, as she described her sacred body being stripped away - her "temple" lost and in ruins. CONCLUSIONS: Body image may be an overlooked component of health following a spinal cord injury. This narrative emphasizes the importance of unveiling body image experiences after the treatment of a spinal cord injury to medical professionals. Lessons of the importance of considering the transitioned body experiences after a spinal cord injury may help prevent body-related depression and other subsequent health impacts. Recommendations for best practice are provided. Implications for Rehabilitation Spinal Cord Injury • A spinal cord injury may drastically change a person's body image, thereby significantly impacting psychological health • More effective screening for body image within the medical/rehabilitation context is needed to help practitioners recognize distress • Practitioners should be prepared to refer clients to distress hotlines they may need once released from treatment.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".