Peer charity donation, gratitude, and self‐esteem among left‐behind children
Bibliographic record
Abstract
Previous research suggests that receiving a charity donation could induce gratitude but threaten self-esteem. We investigated if peer charity donations from typical children benefit or harm the mental health of their left-behind children (LBC) classmates. We recruited children at a school (i.e., intervened school) that organized peer charity donations every semester and three typical schools (i.e., non-intervened school) without such intervention in China. Participants completed the gratitude, self-esteem, depression, and social anxiety scales. A statistical toolbox, "Matchit", randomly selected 420 children aged 9-13 (220 females, 200 males, 213 LBC, 207 non-LBC); there was no significant difference in left-behind status, age, gender, or family economic status (all p > .10) between the intervened and non-intervened groups (210 per group). Structural equation model analyses revealed that gratitude was associated with higher self-esteem, lower social anxiety, and lower depression. Moreover, the intervention effect on self-esteem was significantly positive among the LBC recipients and non-LBC donors. The interaction between intervention and left-behind status was significant on gratitude and depression. Specifically, the intervention effect was not significant on gratitude or depression among the LBC but was significantly negative on gratitude and depression among the non-LBC. Peer charity donation may increase self-esteem among children (recipients or donors) via increased social connection or satisfaction of basic needs, yet decreased gratitude among the donors due to the "moral licensing effect".
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".