Factors affecting anticipatory grief of family carers supporting people living with Motor Neurone disease: the impact of disease symptomatology
Bibliographic record
Abstract
Objective To investigate the effect of carer- and disease-related factors on anticipatory grief (AG) in family carers supporting people living with Motor Neurone Disease.Methods Seventy-five carers from the UK and USA participated in this cross-sectional study, between July 2021 and February 2023. Participants completed assessments on: anticipatory grief (MMCGI-SF, comprising three sub-scales: Personal Sacrifice Burden, Heartfelt Sadness and Longing, Worry and Felt Isolation); person with MND (pwMND) behavioral changes (MiND-B) and disease severity (ALSFRS-R); carer-pwMND emotional bond (Relationship Closeness Scale), familism levels (Familism Scale), and reported hours of care provided. Multiple linear regression analyses were conducted to explore factors impacting carer AG.Results AG total scores showed that 50.7% of carers were experiencing common grieving reactions, 22.6% presented intense grieving emotions, and 26.7% presented low grieving responses.Disease severity (regression coefficient, β = −0.31, p = 0.01, 95%CI −0.91 to −0.13) and behavioral changes (β = −0.34, p = 0.002, 95%CI −1.45 to −0.33) predicted AG total scores (proportion of explained variation, R2=0.38, p < 0.001).Regarding AG subscales, Personal Sacrifice Burden (R2=0.43, p < 0.001) was predicted by disease severity (β = −0.39, p < 0.001, 95%CI −0.42 to −0.11). Behavioral changes predicted Heartfelt Sadness and Longing (β = −0.27, p = 0.03, 95%CI −0.49 to −0.03; R2 = 0.21, p = 0.01) and Worry and Felt Isolation (β = −0.42, p < 0.001, 95%CI −0.63 to −0.20; R2=0.33, p < 0.001).Conclusion This study suggests that disease-related factors may be the strongest predictors of carer AG. Interventions addressing carers’ understanding and management of MND symptoms seem crucial to support their experiences of loss and their acceptance of MND. Evidence-based support for carers in MND services is required.
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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 | 0.000 |
| 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".