Bibliographic record
Abstract
Housing makes a difference to our health. Decent, safe, and affordable housing contributes to our mental and physical well-being, while inadequate housing or even homelessness can do the opposite. Having a pre-existing mental illness or substance use issue often restricts a person's options to access, afford, and maintain the kind of home that would enhance and promote recovery. On the foundation of reviewed literature, as part of a practicum placement with Northern Health and Mental Health and Addictions, I undertook this quantitative, descriptive study in Prince George, and set forth to develop an understanding of the need and type of housing required for individuals with a serious and persistent mental illness (SPMI). As well, I took a look at the current housing available in Prince George, BC Canada, including speaking with landlords and in some cases, doing some education around mental illness as there was clearly some stigma present. A survey questionnaire to learn from people with SPMI was prepared and conducted at three separate locations in Prince George. Participation was completely voluntary. The second part of my practicum project involved developing an Iportal system in which information on current housing availability became assessable to the case managers on the Community Outreach and Assertiveness Team (Coast Team). The Coast team works with individuals who have a serious and persistent mental illness that is chronic in nature. This is an important part of my practicum as case managers are continuously looking for adequate housing for their clients and by having a system in place such as the Iportal, it will substantially reduce the number of hours spent on trying to find housing. I hope to share the final results and recommendation stemming from my study with those individuals at the decision making levels. In Prince George, that would include upper Managers in Northern Health's Mental Health and Addiction services. --P. [i]-ii.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".