Any time and place? Digital emotional support for digital natives.
Bibliographic record
Abstract
Digital natives (i.e., those who have grown up in the digital age) are likely to receive emotional support through digital means, such as texting and video calling. However, virtually all studies assessing the benefits of emotional support have focused on in-person support; the relative efficacy of digital support remains unclear. This study assessed a sample of young adults' negative emotions, digital and in-person support for those emotions, and success in regulating them 3 times per day for 14 days (N = 164; 6,530 collective measurement occasions). Participants' social surroundings at the time of each negative emotion and trait levels of social avoidance were also considered. Digital support was expected to be received more often and perceived as more effective for regulating negative emotions when participants were alone and higher in social avoidance. However, with the exception of those higher in social avoidance receiving less digital (and in-person) support, digital support was received and perceived as effective regardless of these factors, and its perceived effectiveness was on par with that of in-person support. For digital natives, digital support may be just as effective as the "real thing" and its benefits may not be restricted to isolated or socially avoidant users. Findings are discussed in relation to the emotional consequences and social constraints of the COVID-19 pandemic. If transcending the time and space limitations of in-person support with digital support is the new norm, the good news is that it seems to be working. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".