Mixing Methods and Sciences: A Longitudinal Cross-Disciplinary Mixed Methods Study on Technology to Address Social Isolation and Loneliness in Later Life
Bibliographic record
Abstract
Despite a growing interest in longitudinal mixed methods research, the literature offers few examples of complex designs. To evaluate a communication-based technology to address social isolation and loneliness in later life, we conducted two long-term studies in aged-care homes. We used a longitudinal convergent mixed methods design and a cross-disciplinary approach that employed techniques from social and computer sciences to ensure a comprehensive evaluation. While cross-disciplinary mixed methods research is also growing, a discussion of its methodological practices, challenges, and strategies is still scarce. This article contributes to mixed methods research by providing lessons learned on how cross-disciplinary mixed studies can be designed and integrated from collection to interpretation, particularly when combining convergent and longitudinal approaches. We also show the value of “design-in-action”—that is, the refinement and adjustment of techniques throughout research, as methods “talk to each other.”
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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 | 0.082 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".