Learning from past stories of success: values, skills, and attitudes as key determinants of first year postsecondary education completion among Nunavimmiut in Montreal
Notice bibliographique
Résumé
Postsecondary Education (PSE) is a key determinant of health.Despite evidence of academic ability, Indigenous youth have the lowest PSE attainment rates of any other cultural or ethnic group.While there is a growing body of literature on barriers to graduation for Indigenous students, many institutions continue to struggle with how best to retain first year students-a critical year in setting the foundation to graduation.This is in part driven by limited research on determinants or strategies for success.Looking retrospectively at the experiences of Nunavimmiut students in their first year through semi-structured interviews and through textual analysis of public Facebook® posts, this thesis adopts an asset-based approach and identifies three key determinants for first year completion-values, skills, and attitudes.Values were elements of the PSE experience which were meaningful to students and included factors such as personal growth and healing, whereas skills were tools and competencies for success such as study skills and goal setting.Attitudes were ways of thinking or feeling towards PSE such as fostering a sense of belonging.For existing student services or those just starting out, this study provides a road map for increasing Indigenous student retention in the first year.It does this through identifying both three broad concepts supporting student success as well as concrete actions within each area which have proven helpful to past students.In addition, this study found that values, skills, and attitudes supported retention through helping students build a 'sense of place' in the PSE environment.These findings may prove useful to addressing student retention by reframing the issue as a question of "what builds sense of place on campus?"This project was approved by the Research Ethics Committee of Nunavik's school board, Kativik Ilisarniliriniq, McGill University, and John Abbott College.According to the Community Well-Being Index calculated by Statistics Canada, rather than improving over time, the gap in educational attainment between Indigenous and non-Indigenous Canadians (2/3 weighted for high school completion, 1/3 weighted for university degree completion) has grown considerably since 2001(Aboriginal Affairs and Northern Development Canada, 2015).Among Indigenous communities, addressing this education gap is seen as a priority issue, not only as a potential driver of "collective wellbeing", but also as a means to provide youth with the skills and credentials to challenge oppression and succeed in contemporary Indigenous communities (Anuik, Battiste, & George, 2010;Battiste, 2002;Castagno & Brayboy, 2008;Holmes, 2006).Rates of educational attainment vary widely in Canada, with the lowest rates of both high school and PSE completion concentrated among Inuit living in Nunavik, in Northern Quebec (see table 1).According to the 2016 census, 59.0% of Inuit ages 25-64 living in Nunavik did not hold a high school diploma, while 24.8% held a PSE diploma or degree (trades and apprenticeships represented the majority of PSE certifications, and 2.9% were at the CEGEP, college, or university level).When compared to the minority non-Inuit population living in Nunavik, 2.7% had not completed high school while 87.6% held some form of PSE certification, with a larger share of certifications at the CEGEP, college, or university level (54.1%).The gap in educational attainment between Inuit and non-Inuit in Nunavik is partly driven by non-Inuit moving North to fill jobs for which local people lack the credentials, particularly at the PSE level.Filling jobs in Northern communities with Inuit employees is linked to several determinants of health including higher income and a sense of purpose and autonomy (National Collaborating Centre for Aboriginal Health, 2017).For this reason, increasing the number of PSE degrees held by Inuit in Nunavik is an important step towards improving the health and well-being of the population.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».