Taking the bad news with the good: The climate crisis and care for older people
Bibliographic record
Abstract
Taking the bad news with the good: The climate crisis and care for older peopleAs nurses, we quickly become expert at helping people weigh bad news with the good, helping them to move forward at critical moments of their lives.We aid those in our care to put what they learn about their health and what they can do in perspective.Our knowledge and skill as nurses provide the foundation from which we show people they can do things they thought they could not.As gerontological nurses, we partner with those for whom we care, along with other members of their social and health care teams, to support those individuals and families in achieving aims they believe challenging or even impossible.No matter how dire the news, we are there to listen, reflect and coach to build a new sense of the future with individuals and families.We are, by nature, doers.We interpret information about health and well-being, place it in context
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.010 | 0.017 |
| 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".