Conceptualizations of help-seeking for mental health concerns in First Nations communities in Canada: A comparison of fit with the Andersen Behavioral Model
Bibliographic record
Abstract
This qualitative study explored the fit between on-reserve First Nations community members' conceptualizations of help-seeking for mental health concerns and the Andersen Behavioral Model of Health Services Use. Youth, adults and elders (N = 115) living and or working in eight distinct First Nations communities within a tribal council area in Canada participated in focus groups or individual interviews that were transcribed, coded and then analyzed using a thematic analysis approach informed by grounded theory methodology. Resulting themes were then mapped onto the Andersen Behavioral Model of Health Services Use. Participants' conceptualizations of predisposing characteristics including social structures, health beliefs and mental illness, enabling and impeding resources had a high degree of fit with the model. While perspectives on perceived need for mental health care, and spirituality as a health and lifestyle practice had only moderate fit with the model, these domains could be modified to fit First Nations' interpretations of help-seeking. Participants' perceptions of avoidant strategies and non-use of mental health services, however did not map onto the model. These findings suggest conceptualizations of help-seeking for mental health issues in these First Nations communities are only partially characterized by the Andersen Behavioral Model, suggesting there are a number of considerations to Indigenize the model. Findings also highlight potential explanations for why some members of this population may not access or receive appropriate mental health treatment. Multi-pronged efforts are warranted to link culturally normed pathways of help-seeking with effective mental health supports for First Nations community members in Canada.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".