Bibliographic record
Abstract
Grief and loss are commonly encountered in family practice. Patients grieve the loss of loved ones, jobs, marriages, pets and even physical or psychological functioning. Patients may mourn the loss of their good health and seek comfort, understanding, respect, and especially hope. The "work of grief" is a progression through stages of shock, anger, painful dejection, loss of interest in the outside world, inhibition of activity and the temporary interruption of the capacity to love. 1 In addition to a profound sense of sadness, there may be feelings of anxiety, anger, fear, depression, and guilt. Patients, their families, and physicians may underestimate the impact of loss on health, which may cause physical symptoms such as pain, headache, dizziness, fatigue, and disturbances of sleep and digestion, as well as psychological symptoms. Family physicians (FPs) are in a unique position to influence prevention, early detection, and morbidity of these disorders. Psychotherapy can relieve the self-destructive anger and guilt, advance the recovery phase, and stimulate psychological strength and personality growth. How can we identify those individuals who are at risk for grief reactions in our practice? What techniques can FPs utilize to help patients go through the stages of grieving? What supports can the physician provide to families who are experiencing difficulty adapting to loss? How can FPs be aware of their own feelings around loss and how they impact on patient care?
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".