The utility of nostalgia for unhealthy populations: A systematic review and narrative analysis
Bibliographic record
Abstract
Nostalgic reverie (i.e. sentimental longing) has received increased attention as a predictor of health and well-being, but only a handful of reviews have summarized this literature. The available reviews (Dementia: The International Journal of Social Research and Practice, 19, 2020, 330; Intimations of nostalgia: Multidisciplinary explorations of an enduring emotion, Bristol University Press, 2022) left a critical gap in explicating the function of nostalgia among people engaged in unhealthy behaviour. In the current systematic review and narrative analysis, we sought to answer whether and under what conditions emotion serves to help or hinder people engaged in unhealthy behaviours in terms of taking action to change. We identified 14 studies and categorized them into two themes. In Theme I, nostalgising about a time in one's life when one was healthier motivated both readiness to change and action to change unhealthy behaviour. In Theme II, nostalgizing about the perceived benefits of engaging in unhealthy behaviour (e.g. social connectedness related to drinking) was associated with the continuance or acceleration of the unhealthy behaviour. This review highlights not only the presence of a link between nostalgia and unhealthy behaviour but also that the content of one's nostalgising matters for understanding whether the unhealthy behaviour is undermined or bolstered.
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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 | 0.035 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".