Timely Considerations of Using the de Jong Gierveld Loneliness Scale with Older Adults Living in Long-Term Care Homes: A Critical Reflection
Bibliographic record
Abstract
<strong>Context:</strong> Despite being widely used with older adults in the community, there is limited literature on using the de Jong Gierveld Loneliness Scale with older adults living in long-term care (LTC). <strong>Objective:</strong> The purpose of this article is to discuss the considerations of using this scale with older adults in LTC. <strong>Method:</strong> Our team consisted of older person and family partners, a clinician, and academic researchers working together in all stages of research using the Loneliness scale to conduct individual interviews with 20 older adults in LTC in Vancouver, Canada, as part of a study exploring the experience of loneliness during the COVID-19 pandemic. Team reflection was embedded in the research process, with reflection data consisting of data transcripts, field notes, and regular team meeting notes. Thematic analysis was employed to identify lessons learned and implications. <strong>Findings:</strong> Participants had various challenges responding to the scale. Our analysis identified five themes: a) diverse meanings of loneliness, b) multi-faceted factors of loneliness, c) technical challenges, d) social desirability, and e) situational experience. We also offer five recommendations to consider when using this scale with older adults in LTC. <strong>Limitations:</strong> We used this scale with a small sample of older adults in LTC, which is a more time and labour-intensive population. Data on marital status and educational background was not collected but might help in understanding considerations for using the scale with older adults in LTC. <strong>Implications:</strong> We offer practical recommendations for using the scale with older adults in LTC, especially how qualitative open-ended questions can complement the scale by providing useful insights into context and complex experiences.
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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 | 0.176 | 0.293 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.006 | 0.024 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".