The return home: Disability experiences of Second World War Veterans with amputations
Bibliographic record
Abstract
Introduction: After the Second World War, efforts to support Veterans who experienced traumatic injuries, including amputations, involved rehabilitation, devices, and employment supports. Oral histories can support an understanding of how these Veterans experienced disabilities and community reintegration. The purpose of this project was to address the research question, "How did Canadian Second World War Veterans with amputations experience disability from the time of amputation to 1995?" Methods: A conventional inductive approach was used to analyze 11 transcripts from archival materials of interviews conducted in 1995. Participants were nine male Second World War Veterans with an amputation (four spouses also participated) and two widows of deceased Veterans with amputations. Results: Four main themes emerged: 1) support from Veterans' organizations as affecting reintegration, 2) challenges to and facilitators of community reintegration, 3) the impact of amputation on disability self-perception, and 4) the impact of amputation on aging processes. Discussion: Previous research suggests that the Second World War served as the impetus for rehabilitation therapy. Data from these interviews suggest that Canadian Veterans benefited from supports from Veterans' organizations, as well as government, but were also affected by stigma associated with their disabilities and shouldered much of the responsibility for successful reintegration.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".