De Qualitative Health Research-conferentie in Utrecht
Bibliographic record
Abstract
Van 11 tot en met 13 mei 2005 vond in Utrecht de elfde Qualitative Health Research-conferentie plaats. Deze conferentie is een van de initiatieven van professor Janice Morse, verpleegkundige en antropologe, verbonden aan de Universiteit van Alberta (Canada) en hartstochtelijk voorstandster van kwalitatief onderzoek. Zij is onder meer hoofdredacteur van het tijdschrift Qualitative Health Research en hoofd van het IIQM, het International Institute of Qualitative Methodology, een multidisciplinair internationaal instituut, dat zich ten doel stelt de kwalitatieve methoden binnen academische disciplines te bevorderen (zie website IIQM). De divisie Verplegingswetenschap van de Universiteit Utrecht maakt sinds 2001 deel uit van de IIQM en had het als taak op zich genomen om de jaarlijkse conferentie in Europa te organiseren. Dit jaar was het thema 'Qualitative Evidence in Health Care'. Uit 22 landen waren deelnemers afkomstig om een paper of poster te presenteren.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.054 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.006 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".