Bibliographic record
Abstract
The aim of this thesis is to explore how men in recovery from substance use conceptualize challenges and use their inherent strengths to navigate them. Estimates of substance use prevalence rates in Canada suggest that approximately one-in-five Canadians will meet the diagnostic criteria for substance use disorder throughout their lifetime (Pearson, Janz & Ali, 2013). These prevalence estimates represent a significant cause for concern as addiction is a significant phenomenon in contemporary Canadian society. A scoping literature review was conducted on nineteen research studies to investigate the state of current research. Although there is a wealth of studies on addiction, research frequently utilizes pathologizing approaches. These approaches are commonly focused on addiction in the context of a social or individual issue ripe with problematizing discourses. Six qualitative semi-structured interviews were conducted on men living in recovery-based supportive housing for this research to explore their experiences from the onset of substance use into substance use and recovery efforts. A strength-based perspective was adopted for analysis purposes in order to more accurately reflect on the inherent strengths of the participants. The men provided in-depth accounts of their experiences, conceptualized challenges to their recovery and contrary to many contemporary research discourses, demonstrated an affluence of strengths while articulating challenge navigation. The men were also proactive in their ability to anticipate future challenges and conceive methods of effective challenge navigation. Findings of this study are analyzed and discussed in the context of recognizing the magnitude of the challenges facing the men in recovery and the incredible strengths that they demonstrate in challenge navigation. The implications for current social work practice are outlined and several suggestions are tabled with the goal of improving current practice methods. Finally, suggestions for future strength-based addiction research are offered with the intention of seeking overall improvements to the research field and addressing existing gaps within the literature.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".