Drawing from experience: exploring identity with individuals \nhealing from brain injury
Bibliographic record
Abstract
Acquired brain injury often leaves an individual with long-term physical, cognitive and emotional impairments which can greatly impact their sense of self and relationships with others. Much of rehabilitation literature is focused on what is needed for the individual to return to a pre-injury ‘normal’, however that notion is rooted in a medical model of disability which puts most of the responsibility of recovery on the individual. This compounds the burden the patient and their caregivers have to carry. This study seeks to investigate the journey of recovery through a social model of disability to understand what a healing and supportive environment might look like to meet an individual where they are. Ten participants across Ontario living with brain injury were recruited from brain injury support groups and word of mouth. Qualitative art-based methods were used to deeper investigate the phenomenology of brain injury and its relationship with identity.Individual interviews and drawings produced rich data on the complex and diverse lived experience, building on previous arts-based studies with brain injury survivors. \n \nStudy findings offer further exploration into the lived experience of people living with brain injury through the challenges to their sense of self and coping mechanisms and solutions. Discussion centres around what shifts are needed in society to accommodate people with brain injury. Findings can also inform approaches for healthcare professionals and service providers. The research design intends to make a contribution to arts-based and participatory approaches in the current COVID-19 context, and to inform future researchers who intend to conduct research with remote participants who may often be excluded from in-person studies. Future work involves presenting the findings to family, friends, caregivers and professionals who work with brain injury through an arts-based knowledge transfer piece.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.028 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".