Aloneness need not be lonely: varieties and predictors of positive solitude experiences in daily life
Bibliographic record
Abstract
Background: Despite our basic need for social connectedness, solitude is a normal and sought-out part of life. However, previous research often links solitude (the state of being alone) with loneliness, stress, and disease risk, leaving potential benefits of solitude, such as self-reflection and sense of control, relatively understudied. The current study aims to identify the thought patterns and affective states characterizing a full range of both negative and positive solitude experiences occurring in daily life and to identify key individual difference factors shaping the likelihood of experiencing potentially health-promoting kinds of solitude. Methods: 100 adults age 50+ and 50 university students in Greater Vancouver completed up to 30 momentary assessments over 10 days assessing current and desired social situation, thoughts, and affect. Latent class cluster analyses will be used to categorize solitude episodes into distinct types based on thought-affect profiles. Next, a set of individual difference factors will be used to predict the likelihood of experiencing each identified type of solitude. Expected results: It is hypothesized that two or more solitude types will emerge from exploratory analyses, that age, introversion, openness to experience, preference for solitude, and social network strength will predict more positive solitude experiences, and that neuroticism, social anxiety, and loneliness will predict more negative experiences. Current stage: All data collection is completed and analyses underway. Discussion: This study will provide a new framework for conceptualizing solitude by incorporating thought-sampling into ecologically valid time-sampling assessments, and will identify key traits that predict individuals’ propensity to thrive when alone.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".