Dare to Dream: Promoting Indigenous Children's Interest in Health Professions through Book Collections
Bibliographic record
Abstract
Introduction: Indigenous peoples in Canada experience significant health challenges, but few pursue careers in the health sciences. Two programs by medical librarians designed to encourage children in First Nations communities to dream of careers in the health professions will be presented. Description: An academic library in [Province] developed children’s health and science book collections with Indigenous school libraries. Library and information science students, as well as a librarian, participated in health education activities in the recipient schools. This project inspired the community service project of the joint MLA/CHLA-ABSC/ICLC Mosaic|Mosaïque 2016 conference, which focused on placing similar collections in Ontario Indigenous communities. The mechanics, benefits, and challenges of the programs will be discussed including book selection and delivery. Outcomes: Hundreds of books have been delivered and informal qualitative evaluative data from the recipient communities indicates positive outcomes. Some difficulties in providing optimal access to the books were identified due to communication problems or the relative lack of library infrastructure in these communities. Discussion: Reading for pleasure is linked to student's academic success. Access to varied and quality literature is important for school achievement, therefore these collections may potentially impact student’s future life chances. While a direct correlation between these collections and student’s future career choices cannot be easily measured, it is known that Indigenous high school graduates frequently choose to pursue professions linked to the needs of the community. Therefore any materials drawing attention to potential community health needs may well influence student’s choices.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
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".