Understanding hope from the voices of service users and providers across Canada
Bibliographic record
Abstract
Although Canada is home to the second largest non-profit and volunteer sector in the world, there is an absence of an overarching framework to guide human services delivery (Hall et. al., 2005; Rahmani, 2022). This paper documents the first phase of a three-year study that seeks to begin to bridge this gap by learning from both HS providers and users’ narratives, specifically in relation to the topics of hope, self-compassion, and authentic collaboration. The first phase of the research focused on the topic of hope via the following questions: How do HS consumers and service providers meaningfully experience hope in the course of HS service delivery within their lifeworlds? How might these experiences inform a guiding framework for Canadian HS service delivery? A thematic analysis of surveys and interviews collected from six partner organizations across Canada revealed the following themes: 1) the importance of human connections; 2) the building and evolution of hope; and 3) the futurity of hope. These findings point out several implications for practice and research, including a need for human-centred training that focuses more on topics like sensitivity and compassion. Respondents, particularly the service providers, also spoke to the need for strategies and opportunities to take care of oneself physically, mentally, and spiritually. This call is especially prevalent in the wake of the COVID-19 pandemic and funding cuts across Canada.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.036 | 0.015 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| 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".