Changes over time in the management of long‐term conditions in primary health care for adults with intellectual disabilities, and the healthcare inequality gap
Bibliographic record
Abstract
BACKGROUND: Quality of primary healthcare impacts on health outcomes. This study aimed to quantify trends in good practice and the healthcare inequalities gap. METHOD: Indicators of best-practice management of long-term conditions and health promotion were extracted from primary healthcare records on 721 adults with intellectual disabilities in 2007-2010, and 3638 in 2014. They were compared over time, and with the general population in 2014, using Fisher's Exact test and ordinal regression. RESULTS: Management improved for adults with intellectual disabilities over time (OR = 5.32; CI = 2.69-10.55), but not for the general population (OR = 0.74; CI = 0.34-1.64). However, it remained poorer, but to a lesser extent, compared with the general population (OR = 0.38; CI = 0.20-0.73 in 2014, and OR = 0.05; CI = 0.02-0.12 in 2007-2010). In 2014, health care was comparable to the general population on 49/78 (62.8%) indicators. CONCLUSIONS: The extent of the healthcare inequality gap reduced over this period, but remaining inequalities highlight that further action is still necessary.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".