COVID-19 pandemic and physical health screening in an assertive community treatment service
Bibliographic record
Abstract
Introduction Patients with severe mental illness experience physical health inequities. They are less likely to receive preventative care and adequate treatment for physical illnesses. Physical health screening of patients receiving antipsychotics is usually carried out every six months. This comprises screening bloodwork and ECGs, and the sharing of results with family physicians. Objectives We sought to investigate whether the pandemic affected the receipt of routine physical health screening in patients managed by an Assertive Community Treatment (ACT) Service. Methods A comprehensive chart review was performed on 62 ACT patients. We compared the receipt of screening bloodwork and ECGs from March—December 2020 to the same period in 2019. Results were analyzed using McNemar’s Chi square test with Yates’ correction. Results Patients were less likely to have received an ECG during the pandemic period. 69% received an ECG from March—December 2019 versus 42% from March—December 2020 (χ 2 = 7.76, p<0.01). Similarly, patients were less likely to have received screening bloodwork during the pandemic period (69% vs. 50%, Χ 2 = 4.32, p<0.05). Qualitative discussion with ACT staff regarding the 39 patients who had not received an ECG and/or bloodwork during the pandemic period revealed system-related (8%), patient-related (23%), and Covid-related (69%) barriers to screening. Covid-related barriers included transport concerns and lab closures. Conclusions ACT patients were less likely to have received routine health screening during the Covid-19 pandemic. Thus, the pandemic may have exacerbated physical health inequities for patients with severe mental illness. Covid-related barriers to screening represent an important target for intervention. Disclosure No significant relationships.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".