Health Behaviors in Times of COVID: Different Sources of Support for Older Adults
Bibliographic record
Abstract
Abstract Eating a nutritious diet reduces vulnerability to common chronic diseases. Yet, older adults struggle to meet nutritional guidelines; many have found it particularly challenging to access fresh foods such as fruits and vegetables during the pandemic. Thus, it is vital to better understand how older adults may recruit the help of close others to support healthy dietary intake. This COVID-19 study examines the role of support for promoting fruit and vegetable consumption in daily life. Ninety-seven older adults participated with a close other of their choice (62 % spouse; 38% non-spouse Mage partner1 = 72, SD = 5.26, Mage partner2 = 62, SD = 16.38). Both partners completed two daily questionnaires for 10 days. In the morning they reported their intentions for eating fruit and vegetables. In the evening, they noted their consumed fruit and vegetable servings, the extent to which this matched their intentions, and their partners support in doing so. Consistent with previous research, the older participants were, the more they consumed fruits and vegetables. On days when participants received more support from their partner, they were more successful at reaching their dietary goals. Interestingly, initial findings suggest that associations were stronger when support was provided from a non-spouse than if the support came from spouse. Follow-up analyses, with a larger sample, will further examine some of the underlying mechanisms so as to better understand the role of different kinds of support providers during the pandemic and shed light on who may be best suited to provide support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".