Engaging with discursive complexities in mental health accessibility: Implications for acquired brain injury
Bibliographic record
Abstract
The psychosocial needs of people with acquired brain injury (ABI) have been neglected based on ableist assumptions of incapability to participate in mental health treatment. Although people without disabilities benefit from evidence-based mental health supports, these treatments remain inaccessible for those with disabilities after ABI. Discursive simplifications used in dominant conceptualisations of health and disability may maintain this inaccessibility. This paper examines the role of discursive constraints in concealing the complexities of ABI recovery, undermining the gradients of mental health exclusion among different ABI subpopulations, and muddying possibilities for enhancing mental health accessibility. An alternate discourse that challenges disabling societies in service of centring the whole person is proposed. Discursive opportunities are thus created by conceptualising the objective and subjective dimensions of disability as intermeshed, providing both the motivation to incentivise mental health inclusion, as well as a method to achieve it. By recognising the unavoidable impact of bodily impairments on social participation, participatory ideals can be actualised by accommodating ABI-related disabilities in mental health treatments. The possibilities for transformative research and practice are illuminated through examples of mental health treatments that have been preliminarily adapted using accommodations, and a research agenda for realising these possibilities is proposed.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".