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Record W7062917994

When private self-compassion goes public: effects of social media self-disclosure

2024· dissertation· en· W7062917994 on OpenAlexaff

Bibliographic record

VenueMspace (University of Manitoba) · 2024
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsEmbarrassmentAffect (linguistics)PopularitySocial mediaTask (project management)Test (biology)Shame
DOInot available

Abstract

fetched live from OpenAlex

Both practicing self-compassion and sharing difficult experiences with trusted others to feel less alone in suffering can help individuals decrease their negative affect (Masaviru, 2016; Neff, 2003). When completed privately, self-compassion exercises can partly reduce negative affect following a shame-recall exercise, and self-disclosure literature suggests that sharing private information with a select few can benefit the individual. Social media’s popularity has brought together public disclosures, and discussions of difficult experiences. Contrary to the traditional use of social media in which users seek approval from their audience (Pinkerton et al., 2017; Sheldon & Newman, 2019), social media users are beginning to share posts about their imperfections while being self-compassionate. This research examined whether it is beneficial for university students to use social media to publicly engage in typically private practices of self-compassion. I hypothesized that those who wrote about difficult experiences with self-compassion in a public manner would experience higher negative affect, less emotional relief, and greater desire for reassurance than those who wrote in this way privately. To test this hypothesis, in Study 1 I developed and evaluated an embarrassment-recall task because sharing of embarrassment can be especially helpful in reducing this uncomfortable emotion (Leary et al., 1996). Participants in Study 1 rated their affect before and after an embarrassment-recall and a self-compassion induction. Study 2 included two independent projects: Study 2a used online questionnaires to examine correlations of participant traits and social media behaviours, while Study 2b used the embarrassment task from Study 1 to consider how variations in the expectations of privacy (private vs. public) for self-compassionate writing affected how participants evaluated their self-compassionate writing and whether they endorsed hopes related to reassurance-seeking. Participants imagined re-reading or sharing their writing on social media and then rated their hopes and affect. Study 1 demonstrated that the novel embarrassment recall task performed comparably to the existing shame recall tasks in that negative affect reduced at a similar rate for both types of recall following a self-compassion induction, forming the basis for Study 2. Study 2a demonstrated that higher trait self-compassion and lower reassurance-seeking were related to social media posting behaviour. Study 2b demonstrated that participants who imagined posting their writing to their social media had few hopes for their writing, whereas those who wrote privately were hopeful that their writing would benefit them in multiple ways. The discussion considers how the results help to broaden our understanding of social media, self-compassion, and provide new experimental methods for advancing self-compassion research.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.008
GPT teacher head0.197
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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