Psychological adjustment following severe lower limb trauma: A repertory grid study
Bibliographic record
Abstract
The study explores prospectively how people come to terms with a severe injury to a lower limb following acute trauma. Participants were adults admitted to plastic surgery or orthopaedic surgery wards with open tibial fractures undergoing reconstructive surgery. Participants were seen within six weeks of injury, and asked to complete three short questionnaires (the Hospital Anxiety and Depression Scale, the Impact of Events Scale, and the Short-form McGill Pain Questionnaire). In addition, participants completed a repertory grid using self and social role elements. After a three-month interval participants were sent the questionnaires and were asked to rerate their repertory grid elements according to the constructs generated in the initial interview. The repertory grid data was analysed using Principal Component Analysis and by examining construct correlations and inter-element distances. The constructs generated were also considered thematically. Data from the questionnaires was used primarily to describe the population and changes over time. The results are discussed in relation to psychological theory and previous research findings on adjustment, trauma, self-concept and construct systems. Findings indicate that the personal meaning of lower limb trauma influences the impact on the self-concept, and that changes in the construct system are apparent over time suggesting a process of adjustment to injury. Limitations of the present study are discussed and the implications of the findings for service provision and further research are presented.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".