Consequences of self-esteem concealment on well-being
Bibliographic record
Abstract
Low self-esteem is devalued and viewed as a flaw in North American culture (e.g., Cameron, 2016; Cameron, MacGregor & Kwang, 2013; Zeigler-Hill & Myers, 2009), therefore people with lower self-esteem are motivated to conceal it from those around them (Cameron, 2016). The present study attempted to examine whether the act of concealing insecurities (i.e. lower self- esteem) had subsequent impact on well-being. One hundred and eighteen participants, recruited from introductory psychology classes, recorded a video of themselves answering questions to be emailed to a parent or parental figure of their choosing. They were randomly assigned to either conceal their insecurities from their family member (Concealment Condition), or just be themselves (Be Yourself Condition). Well-being was assessed as the presence of authenticity, positive affect, and life satisfaction, and the absence of negative affect and fatigue. Results demonstrated that self-esteem has a prominent impact on well-being, with a main effect of self- esteem on all five measures of well-being. Findings regarding the interaction between self- esteem and condition were inconclusive, due to issues with adherence to the manipulation instructions. Reported self-esteem concealment was significantly correlated with three measures of well-being: authenticity, negative affect, and life satisfaction. Additionally, reported self- esteem concealment partially mediated the relationship between self-esteem and authenticity. Future research investigating the causal order between the constructs of self-esteem, self-esteem concealment, and well-being is suggested.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".