Healthcare ‘Fit’ and autism: An examination of barriers to, and experiences of, physical healthcare for people on the autism spectrum
Bibliographic record
Abstract
Autistic individuals experience substantial health inequities, reflected in poorer health outcomes and higher mortality rates. One suggested determinant of this health inequity is issues in access to healthcare. This thesis, therefore, aimed to examine the barriers to healthcare for autistic individuals and consider how access might be improved. Five empirical studies were completed. Study 1 comprised a systematic review of barriers to healthcare reported by autistic individuals, caregivers, and healthcare providers (HCPs). A taxonomy of barriers was developed comprising four themes: barriers associated with autism-related characteristics; other patient-related barriers; HCP-related barriers; and system-related barriers. Study 2 described the development and preliminary evaluation of a novel caregiver-report tool to assess barriers to care, which consisted of four factors: patient-related barriers, HCP-related barriers, system-related barriers, and barriers related to managing care. The most frequently occurring barriers included difficulties identifying or reporting pain/symptoms and a lack of HCP knowledge about autism. Study 3 described the development and preliminary evaluation of a physician-report tool to assess barriers to providing care to autistic individuals, which consists of three factors: patient-related barriers; HCP/family-related barriers, and system-related barriers. The most common barriers included insufficient patient supports, and communication difficulties. Study 4 describes the use of patient narratives to identify barriers occurring in challenging healthcare encounters for autistic individuals and assessed the impact these had on patients. Patient-related barriers occurred most often, followed by HCP-related barriers. More than a quarter of the described encounters were rated as high severity. Study 5 presents a systematic review of interventions aimed at improving access to, or experiences in, healthcare for autistic individuals. Interventions were mostly patient-focused with fewer studies targeting the HCP or the system. The data presented herein demonstrate that autistic individuals face substantial health inequities. Thus, models of healthcare must change to ensure optimal health for the entire autistic community.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".