Bibliographic record
Abstract
As smartphone use becomes increasingly pervasive, the phenomenon of partner phubbing (Pphubbing) – diverting attention to a smartphone while in the presence of a romantic partner – has received growing empirical attention. This study addresses gaps in the literature by introducing the Perceived Neglect due to Partner Phubbing Scale (PNPS), the first psychological measure specifically designed to assess feelings of neglect stemming from Pphubbing in romantic relationships. A sample of 102 Canadian adults who reported being in a romantic relationship completed the Partner Phubbing Scale (a behavioural measure), the newly developed PNPS, a relationship satisfaction measure, and demographic items. Reliability analyses, inter-item correlations, and principal components analysis were conducted to reduce the number of items on the PNPS, resulting in a two-component structure with reliable subscales. In accordance with the hypothesized relationships, PNPS scores were positively associated with the behaviour of Pphubbing and negatively associated with relationship satisfaction. These findings provide preliminary support for the PNPS’ construct validity. Supporting an exploratory hypothesis, results indicated that women reported significantly greater perceived neglect than men. These findings align with prior literature, suggesting that women experience stronger emotional reactions to Pphubbing. Limitations and future directions associated with the current study are outlined. Future research should utilize the PNPS to examine additional correlational relationships with variables such as attachment styles, communication patterns, and personality traits. Moreover, incorporating diverse methodologies, such as diary-based designs, may offer nuanced understanding of how individuals in romantic relationships experience perceived neglect due to Pphubbing over time.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.073 | 0.031 |
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".