Experiences of Norwegian Mothers Attending an Online Course of Therapeutic Writing Following the Unexpected Death of a Child
Bibliographic record
Abstract
The unexpected death of a child is one of the most challenging losses as it fractures survivors’ sense of parenthood and other layers of identity. Given that not all the bereaved parents who have need for support respond well to available treatments and that many have little access to further intervention or follow-up over time, online interventions featuring therapeutic writing and peer support have strong potential. In this article we explore how a group of bereaved mothers experienced the process of participating in an online course in therapeutic writing for the integration of grief. Our research questions were: How do parents who have lost a child experience being part of an online course in therapeutic writing? What are the perceived benefits and challenges of writing in processing their grief? We followed an existential phenomenological approach and analyzed fieldwork notes (n= 13), qualitative data from the application and assessment surveys (n= 35;n= 21), excerpts from the journals of some participants (n= 3), and email correspondence with some participants (n= 5). We categorized the results in three meaning units: (1) where does my story begin? The “both and” of their silent chaos; (2) standing on the middle line: a pregnancy that does not end; (3) closures and openings: “careful optimism” and the need for community support. Participants experienced writing as an opportunity for self-exploration regarding their identities and their emotional world, as well as a means to develop and strengthen a bond with their children. They also experienced a sense of belonging, validation, and acceptance in the online group in a way that helped them make sense of their suffering. Online writing courses could be of benefit for bereaved parents who are grieving the unexpected death of a child, but do not replace other interventions such as psychotherapy. In addition to trauma and attachment informed models of grief, identity informed models with a developmental focus might enhance the impact of both low-threshold community interventions and more intensive clinical ones. Further studies and theoretical development in the area are needed, addressing dialogical notions such as the multivoicedness of the self.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".