'You’re someone different': using social identity approach to health, and appraisal theories to understand impact and response to traumatic injury
Bibliographic record
Abstract
Injuries are a worldwide health problem, representing 9% of global mortality. Survivors of traumatic injury may experience loss of function/disability, reduced quality of life, problems returning to work and psychological issues. Despite this impact, evidence on psychological mechanisms that impact recovery and return is fragmented and mostly atheoretical. This thesis applies a multiple-theoretical framework to qualitatively explore the psychological impact and responses to traumatic injury, especially in relation to return to work. \n \nMethodology: \n \nThree data sets were analysed using theoretically informed reflexive thematic analysis informed by appraisal theories and the social identity approach to health. Analysis aimed to explore whether social identity context contributed to stress appraisal and coping processes, accounting for some of the social-psychological contributions to injury perception identified in current literature. \n \nStudies 1 and 3 used data generated (mostly by the thesis author) for a larger programme of research to develop and trial a clinical intervention to support trauma survivors’ return to work (www.rowtate.org.uk). Study 2 data was collected independently by the author with a specific focus on psycho-social mechanisms experienced by trauma survivors. \n \nKey Findings: \n \nSurvivors and service providers appraised work identity threat from impairments that disrupted work participation. The associated disruption of work identity resources (purpose; connection) negatively impacted wellbeing. Survivors perceived new disability as incompatible with valued work identity. Survivors appraised the availability of workplace social support in relation to continuity of work identity. A key theoretical contribution of this thesis is that combining appraisal theories and social identity approach to health allows identifying key mechanisms for recovery and return to work among traumatic injured individuals. \n \nFindings have important practice implications that can be used to a) develop interventions to support recovery (including RTW), b) address gap in understanding between professionals and patients, and c) integrate psycho-social understanding in future research on traumatic injuries.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".