Development of the Carers’ Alert Thermometer for Young Carers (CAT-YC) to Identify and Screen the Support Needs of Young Carers: A Mixed Method Consensus Study
Bibliographic record
Abstract
Abstract This paper reports on a multi-phased, mixed-method consensus-based study conducted with young carers in the UK aged 11–18, and health, social care and education professionals from the UK, USA and Canada, to identify priority items for inclusion in a short screening tool for use with young carers of a family member with a progressive or long-term illness or disability. Following ethical approval from University and local Research Ethics Committees, qualitative and quantitative data were collected between 2017 and 2019 from 267 people (107 young carers; 160 professionals), through interviews, a focus group, a Delphi survey, consensus group meetings and consultations. Qualitative data were analysed thematically, and quantitative data were analysed using measures of central tendency, frequency and levels of dispersion. The resulting Carers’ Alert Thermometer (CAT-YC) contains an identification question followed by ten areas of need across two themes of ‘current caring situation’ and ‘carer’s health and wellbeing,’ along with guidance for possible next steps and space for an action plan to be jointly agreed between the screener and young carer. Preliminary piloting of the CAT-YC provides evidence of identifying and monitoring needs, and is expected to be useful for young carers, a wide range of professionals, and organisations that support young carers.
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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 | 0.183 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".