A Tribute to the Calgary Family Nursing Unit: Lessons That Go Beyond Family Nursing
Bibliographic record
Abstract
When I received an e-mail from the Calgary Family Nursing Unit announcing its 25th anniversary, my immediate thought was: It can’t be. It can’t be that the Calgary Family Nursing Unit has been in existence for such a long time! It can’t be that this unit has been able to accomplish all that it has in just 25 years! Two opposing reactions yet both true. I immediately dashed off a congratulatory note, but in the back of my mind I knew this would not suffice. The occasion warranted much greater recognition than a mere congratulatory e-mail from me. The Calgary Family Nursing Unit, under the visionary leadership of Lorraine Wright, Janice Bell, and WendyWatson, deserved much more. These women deserved to be recognized, celebrated, and feted for their work in family nursing both in Canada and worldwide. [...]
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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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.014 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.040 | 0.060 |
| Insufficient payload (model declined to judge) | 0.060 | 0.017 |
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".