A comparison across cultures of the impact of oral health problems in children
Bibliographic record
Abstract
I started this project work in November 2001. After all the laborious work of literature review, data collection, data analysis and text writing, this thesis is completed. I am so appreciative ofall the people who have helped me making this possible. First and foremost, I acknowledge the supervision of Dr. Paul Allison, who is always a source of advice and assistance whenever needed. He dedicated so much time and effort to organize the step-by-step procedures ofthis project, to give valuable advices for each step, and to make sure that all the details have been taken care of. When writing this thesis, he helped to make sure that the text is complete and easy to understand. Even when I was content, he was not, and I am appreciative of his devotion to making this a better writing. The financial support for this project was from Canadian Institute of Health Research. A special thanks to the two dental clinics (dental clinic of Montreal Children's Hospital and orthodontic clinic of Dr. Go) for the permission and help they provided in data collection, and to Jennifer Golfman, who helped with part of the data collecting work. Also I want to thank all the participating children and their parents for taking time to have the short interviews and complete the questionnaires. Last, I thank my parents for their continued support from the other side of the globe. They gave me the strength for going through all this and going on.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".