How Can Psychologists and Psychiatrists Help COVID-19 Bereaved Persons:Five propositions to Understanding Contextual Challenges
Bibliographic record
Abstract
The COVID-19 pandemic is causing unprecedented cumulative deaths and leaving behind millions of bereaved families and individuals. Moreover, the pandemic is disrupting social fabrics in the conventional way we mourn our deads. In this context therefore, how can psychologists, psychiatrists and other health care professionals help bereaved families and individuals more effectively? This opinion paper proposed five recommendations that cover mental health care needs and challenges which may emerge from the management of these traumatic deaths. In all, efforts to comply with either DSM-5 or ICD-11 PGD guidelines could help COVID-19 bereaved persons with overwhelming distress, as they ensure therapists' use of appropriate terminologies in therapeutic alliances. However, clinicians need to have a global perspective of COVID-19 bereavement courses, political and public health measures due to the pandemic, and flexible attitudes about the ICD-11 and of DSM-5 time-criterion for diagnosis. This paper emphasizes the importance of social and collective recognition of COVID-19 deaths through various symbolic and materialized forms to free up collective and individual capacities for resilience. The necessity of individual and group interventions through online platforms is underscored, however these modes of therapies may not reinforce social inequalities by excluding bereaved individuals who really need them.
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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".