UN COVID 19 Disability Inclusion Strategy: Assessing the Impact on Cultural-Safety and Capability Information Approach
Bibliographic record
Abstract
This paper is aimed at exploring the role of the United Nations Disability Inclusion Strategy as a rights-based concept in understanding the recent COVID-19 outbreak; and how Cultural-Safety Capability Information, visible within the DSM-5, is linked with the achievable recovery and inclusion for persons with disabilities and post-COVID-19 pandemic for health and social care practitioners. There are two measured, actionable targets from the Disability Inclusion Strategy that are geared towards achievable standards of health for persons with disabilities, which are the identifying and eliminating of obstacles and barriers to accessibility in healthcare facilities and the training of healthcare personnel on disability inclusion and improving service delivery for persons with disabilities. The concepts of recovery and inclusion are discussed within a rights-based, and Cultural-Safety Capability Information (DSM-5) approaches in order to curb the COVID-19 info-demic (Information epidemic). This paper has recommendations for the United Nations Disability Inclusion Strategy as a rights-based idea and the re-educating and re-orientation of both the right-holders, persons with disabilities, for example, and the duty-bearers. This paper also discusses the health and social care practitioners and their realisation of health care and recovery, curbing inequalities in accessing health care, education, and easing participation for persons with disabilities during the COVID-19 pandemic
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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 | 0.051 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| 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".