‘Going Missing’ as a Maladaptive Coping Behavior for Adults Experiencing Strain
Bibliographic record
Abstract
This study applied the Threat Appraisal and Coping Theory to explore the mechanisms influencing a person to go missing. We examined the negative emotions and stressors – proximate stressors/stressful events, underlying life stressors, emotional states, and other dysfunctional behaviors – of adults who were reported as missing from 2013-2018. Our results indicate that missing persons experience significant underlying life stressors, stressful situations, and proximate stressors that trigger a missing episode. We also found that most missing adults are described as facing negative emotions, such as anger, and engaging in maladaptive behaviors, such as drug and alcohol use, that are related to these events. These findings, we suggest, highlights that affectual and individual-level mechanisms are influential factors contributing to why adults go missing. Lastly, it was revealed that missing adults are commonly reported as experiencing strains and stressors in their personal relationships, indicating that this phenomenon may be attenuated through social support as an adaptive coping resource. Through these results, we can begin to understand missingness as driven by a negative event, stressor, or emotion in which the person engages in the maladaptive coping behavior of ‘going missing’ as a way to escape the situation and achieve some level of emotional or cognitive distance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".