Reliability and responsiveness of social connection in long‐term care home residents (SONNET) scale
Bibliographic record
Abstract
BACKGROUND: Social connection is important for health and quality of life in long-term-care (LTC) settings, particularly for long term care residents with dementia. However, the field lacks a psychometrically sound tool to measure social connection in a reliable and responsive manner. The Social Connection in Long Term Care Residents (SONNET) scale was recently developed by our team to fill this gap. We have established internal consistency and construct validity for the SONNET scale, yet its reproducibility and responsiveness across time and populations remains to be explored. This study aims to evaluate the test-retest reliability and responsiveness of the SONNET scale among residents of LTC homes located in rural communities, with a focus on those with cognitive impairment, to determine its utility as an outcome measure in intervention studies. METHODS: A prospective, multi-site study will be conducted in LTC settings in Oregon, US. Residents aged 65+ with mild to moderate dementia will be recruited. The SONNET scale will be administered at baseline and after a 3-day interval to assess test-retest reliability. To evaluate responsiveness, a subgroup of participants will engage in a structured intervention to improve social connection, with SONNET administered before and after the intervention period at 3 and 6 months. Floor and ceiling effects will be calculated and considered to exist if >15% of participants scored minimal or maximal scores as their total scores, respectively. Intraclass correlation coefficients (ICC) will be used to assess relative reliability, standard error measurement, minimal detectable change and bland Altman plots will be used to evaluate absolute reliability. Effect size (ES), Standardized response means (SRM), and minimal clinically important difference will be calculated to evaluate responsiveness. RESULTS: We anticipate that the SONNET scale will demonstrate good test-retest reliability (ICC ≥ 0.70) and moderate to high responsiveness (ES ≥ 0.50; SRM ≥ 0.50) in detecting changes in social connection following the intervention. CONCLUSION: Establishing the reproducibility and responsiveness of the SONNET scale will support its use in both research and practice, providing a robust tool for assessing social connection and informing interventions to improve well-being among LTC residents with dementia.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".