It Hurts to Let You Go: Characteristics of Romantic Relationships, Breakups and the Aftermath Among Emerging Adults
Bibliographic record
Abstract
Relationship breakups are common (Connolly & McIsaac, 2009), and difficulty adjusting to the breakup can manifest as post-relationship contact and tracking (PRCT; Lee & O'Sullivan, 2014). Emerging adults (n= 271; aged 18–25; 66% female) provided reports of PRCT after their most recent breakup in the previous year. We examined relationship and breakup characteristics to predict the use of and experience of PRCT. Logistic regression analyses revealed that ex-partner initiation of the breakup and a more intense breakup predicted the use of PRCT, and ex-partner's surprise regarding the breakup predicted being a target of PRCT. A between-subjects comparison of participants who either used or experienced PRCT reported similar impact of PRCT on the self or their ex-partner. However, participants who both used and experienced PRCT reported that the impact that an ex-partner's PRCT had on their lives was more negative than their use of PRCT had on their ex-partner's life, likely reflecting an actor-observer bias in reports. Difficulty adjusting to relationship breakup is normal, and predictive of attempts to remain in contact with an ex-partner. However, the seemingly benign form of contact can have a negative impact on individuals. The findings have implications for those counselling individuals in distress following a breakup, and contribute to the discourse around boundaries after a breakup.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".