ALEXITHYMIA IN FACEBOOK ADDICTION: ABOVE AND BEYOND THE ROLE OF PATHOLOGICAL NARCISSISM
Bibliographic record
Abstract
BACKGROUND: Personality dysfunctions and deficits in the capacity to cope with negative emotional states have been related to internet addictions. However, in relation to Facebook addiction, this issue remains poorly investigated. Specifically, few studies explored the role played by grandiose and vulnerable narcissism in Facebook addiction. Furthermore, the relationship between alexithymia and pathological narcissism has not yet been explored in relation to problematic Facebook use. The main objective of this study was to examine the association among Facebook addiction, pathological narcissism and alexithymia. METHOD: =41.05; SD=14.05) was administered the Bergen Facebook Addiction Scale (BFAS), The Pathological Narcissism Inventory (PNI) and the Toronto Alexithymia Scale (TAS-20). RESULTS: Results showed that pathological narcissism, and especially grandiose narcissism, appears to be an important factor associated to problematic Facebook usage among adults. Moreover, difficulties in the capacity to identify their own emotional states may be a risk factor for such condition. CONCLUSIONS: In conclusion, these results are in line with other data found in the field of behavioral addictions and add further reflection for planning more specific clinical intervention programs for individuals addicted to Facebook with narcissistic traits. These may benefit from a greater focus on the impairments in self-monitoring capacities such as emotional awareness. Personality psychopathologies should be targeted by clinical interventions, but also broader functional impairments (as such as alexithymia) should be addressed as more strategic therapeutic objectives in the field of Facebook Addiction.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".