International clinician perspectives on pandemic-associated stress in supporting people with intellectual and developmental disabilities
Bibliographic record
Abstract
BACKGROUND: People living with intellectual and developmental disabilities (IDD) have suffered disproportionately in health outcomes and general well-being during the COVID-19 pandemic. There is emerging evidence of increased psychological distress. Increased strain has also fallen on clinicians managing the psychological needs of people with IDD, in the context of learning new technologies, staff shortages, reduced services and paused training opportunities. AIMS: To examine clinicians' experiences of patient care, clinical management and the impact of care delivery. METHOD: A mixed fixed-response and free-text survey comprising 28 questions covering four areas (responder demographics, clinical practice, changes to local services and clinician experiences) was developed, using the STROBE guidance. It was disseminated through an exponential snowballing technique to clinicians in seven high-income countries. Quantitative data were analysed and presented with Microsoft Excel. Qualitative data were coded and thematically analysed, and presented with in-text quotations. RESULTS: There were 139 respondents, mostly senior physicians (71%). Two-thirds reported over 10 years working in the field. Quantitative findings include increased clinician stress (77%), referrals (53%), patient distress presentations (>70%), patient isolation (73%) and carer burden (89%), and reduced patient participation in daily activities (86%). A third reported increased psychotropic prescribing. Qualitative analysis outlined changes to clinical practice, particularly the emergence and impact of telehealth. CONCLUSIONS: In the countries surveyed, the pandemic has not only had a significant impact on people with IDD, but also their carers and clinicians. A proactive, holistic international response is needed in preparedness for future public health emergencies.
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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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".