“You're Native but You're not Native Looking”: A Critical Narrative Study Exploring the Health Needs of Aboriginal Veterans Adopted and/or Fostered During the Sixties Scoop
Bibliographic record
Abstract
This study employed a critical narrative approach to examine the experience of Aboriginal Veterans in Canada adopted and/or fostered during the Sixties Scoop. The objectives of this study was to: 1) understand lived experiences of Aboriginal veterans adopted and/or fostered during the Sixties Scoop, 2) investigate health needs articulated by this population, and 3) provide suggestions for the creation of health services to aid Aboriginal veterans adopted and/or fostered during the Sixties Scoop with their health needs. Individual interviews were audio-recorded and conducted with eight participants from across Canada. All interviews were transcribed verbatim and analyzed using the holistic-content model (Lieblich, Tuval-Mashiach & Zilber, 1998). Data analysis of the interviews uncovered three overarching themes: a) sense of belonging, b) racism: experienced and perceived, and c) resilience: not giving up in the face of adversity. Two main health needs conveyed by the participants included mental health care and support to fight substance abuse. More awareness regarding the historical realities experienced by this population and the impact this may have on their overall health is needed. Increased coordination between Veterans Affairs Canada (VAC), Royal Canadian Legion (RCL), National Aboriginal Veterans Association (NAVA), Aboriginal Veteran Autochthones (AVA), and Aboriginal agencies is needed to address the mental health needs experienced by this group of veterans.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.017 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".