Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to investigate the fit between Inuit conceptions of effective helping and Western counselling. STUDY DESIGN/METHODS: The essential components and value foundations of effective Western counselling, including multicultural counselling, were identified from primary and secondary counselling texts. Inuit traditional values and helping practices were identified from the transcripts of interviews with Inuit elders. Interviews with 5 younger Inuit provided information about the counselling needs of contemporary Inuit. Grounded theory analysis of all texts and interview transcripts was used to determine each informant group's conceptions of the elements of effective counselling. A comparative chart was then constructed of the important relationship factors, strategies and process, and effective interventions identified by each informant group. RESULTS: The values and relationship factors of effective counselling are similar in traditional and Western helping, and these same factors are important to the contemporary Inuit interviewed. Affective, behavioural and cognitive interventions were used traditionally; modern generic counselling also uses a variety of strategies from these three primary categories. Cognitive and cognitive-behavioural approaches to problem-solving were traditionally of primary importance, with expression of feelings also seen as essential. CONCLUSION: Western and traditional Inuit helping correspond, and cognitive/cognitive-behavioural approaches especially complement Inuit cultural practice.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| 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".