Bibliographic record
Abstract
This Missing Patients Research Guide contains directions for finding out more about Indigenous patients who entered tuberculosis (TB) sanatoriums and hospitals in Manitoba and never returned home. Part One of the guide presents helpful start-up information. First it explains how to gather useful details including names, dates, and locations that will help in the search as well as how to move forward with your research. Then it outlines three useful “Research Tips”: all of the various names of TB treatment hospitals in Manitoba commonly attended by Indigenous patients; instructions for undertaking database searches using keywords; and techniques for linking information between Indian Residential Schools and hospitals. Last, a “Research Case Study” demonstrates some of the techniques and challenges you may encounter when researching Vital Statistics and Indian Residential School records by looking at the lives of three TB patients, Elie Caribou, Joseph Michel, and Albert Linklater. Part Two of the guide explains how to research the location of patient burials associated with nine hospitals where Indigenous patients were treated in Manitoba, including treatment for TB: Dynevor Indian Hospital, Clearwater Lake Indian Hospital, Brandon Indian Sanatorium, Ninette Sanatorium, St. Boniface / St. Vital Sanatorium, Fort Churchill Military Hospital, Norway House Indian Hospital, Fisher River Indian Hospital and Pine Falls Indian Hospital at Fort Alexander. Some of the general research information found in Part One is repeated under the individual hospitals and sanatoriums along with the specific information that may assist in searching for missing patients at each location. At the end of the guide, in Appendix A, you will find a checklist to help you in your research. Appendix B provides contact information for the organizations mentioned in this guide so that you can reach out by phone, email, or mail. Appendix C discusses accessing the records held by The National Centre for Truth and Reconciliation.
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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 | 0.072 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.477 | 0.245 |
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".