A Comparison of The Health Impacts of Individual Level and Area Based Welsh Government Fuel Poverty Schemes
Bibliographic record
Abstract
IntroductionLiving in a cold and/or damp house is known to increase the risk of morbidity, mortality and excess winter deaths. To reduce fuel poverty in Wales, the Welsh Government developed schemes to provide energy efficiency improvements to those most likely affected by fuel poverty. We explored the relative impacts on health of an individual-level scheme, Warm Homes Nest, and an area-based scheme, Arbed. Objectives and ApproachOverall aim: to evaluate the health impacts of Welsh Government funded schemes designed to reduce fuel poverty. Presented objective: to investigate the relative impact of the individual-level and area-based schemes on the health of recipients. A longitudinal dataset was created using the anonymised residence that received improvements linked to residents’ health measures using routine health records held in the SAIL Databank at Swansea University. We used difference-in-difference (DID) estimations to compare any changes in recipient health before and after intervention with any concurrent change in health in those yet to receive the intervention. ResultsAn analysis of the Warm Homes Nest Scheme, published in 2017 and presented at the IPDLN 2018 conference found a positive impact of the scheme on the health of recipients. This presentation will describe the further analysis comparing the area-based Arbed scheme with both the recipients of the individual-level Nest scheme and groups in comparable need that had not yet received the intervention. We will present results focussing on the relative impacts of the two schemes on respiratory health, infection prescribing and mental health. Conclusion / ImplicationsProviding home energy efficiency interventions has the potential to benefit population health, however there is a scarcity of evidence comparing different methods of implementing schemes. Our findings will inform more effectively focussed home energy efficiency schemes and potentially thus improve the health and wellbeing of people living in Wales.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".