You can’t always give what you want: The challenge of providing social support to low self-esteem individuals.
Bibliographic record
Abstract
It can be challenging for support providers to facilitate effective social support interactions even when they have the best intentions. In the current article, we examine some reasons for this difficulty, with a focus on support recipients' self-esteem as a crucial variable. We predicted that recipients' receptiveness to support would be influenced by both support strategy and recipient self-esteem and that receptiveness in turn would impact providers' perceived caregiving efficacy and relationship quality. Study 1 (hypothetical scenarios), Study 2 (confederate interaction), and Study 3 (reports of recently received support) showed that individuals with low self-esteem (LSEs) are less receptive than are individuals with high self-esteem (HSEs) to support that positively reframes their experience but are equally receptive to support that validates their negative feelings. In Study 4, providers demonstrated some knowledge that positive reframing would be less helpful to LSEs than to HSEs but indicated equal intention to give such support. Study 5 showed that, in a real interaction, friends were indeed equally likely to offer positive reframing to both LSEs and HSEs but were less likely to offer validation to LSEs. LSEs were less accepting of such support, and in turn providers felt worse about the interaction, about themselves, and about their friendship more broadly. Study 6 confirmed that recipients' receptivity to support directly influenced providers' experience of a support interaction as well as their self- and relationship evaluations. The findings illustrate how well-meaning support attempts that do not match recipients' particular preferences may be detrimental to both members of the dyad.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".