FROM PRIVATE LOSS TO PUBLIC EXPRESSION: PHENOMENOLOGICAL STUDY OF DIGITAL GRIEF AMONG 200 LEVELSTUDENTS OFTHE DEPARTMENT OF PSYCHOLOGY NASARAWA STATE UNIVERSITY KEFFI
Bibliographic record
Abstract
In an era where social media has become central to communication and self-expression, the experience of grief is undergoing significant transformation. This phenomenological study explores how Nigerians navigate and make meaning of grief in digital spaces. Using semi-structured interviews with 20 adults who have lost a loved one and expressed their mourning through platforms like Facebook, WhatsApp, and Instagram. The research uncovers the lived experiences behind online grief expressions. Findings reveal five key themes: digital grief as an extension of traditional mourning rituals; continuation of emotional bonds with the deceased; social pressure to perform grief publicly; cultural and religious mediation of online expressions; and the therapeutic role of digital communities in emotional healing. While digital grieving offered participants validation and connection, it also introduced complexities such as performance anxiety and cultural tension. The study concludes that digital grief in Nigeria represents a hybrid mourning space deeply shaped by cultural values, spiritual beliefs, and technological affordances. Implications for grief counseling, mental health interventions, and culturally responsive digital design are discussed. The study calls for further research into intergenerational, gendered, and psychological dimensions of digital mourning in African contexts.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".