Bibliographic record
Abstract
This exploratory study sought to understand the current lived experiences of shelter workers in the Province of Manitoba. Using Concept Mapping as the methodology, a map of their experiences was constructed. Four themes emerged: 1) Shelter worker's beliefs assist in navigating the territories; 2) Insufficient funding of shelters impacts shelter workers' personal and professional life; 3) Organizational culture and values present a vast array of challenges to shelter workers and lastly 4) External agencies and societal responses to domestic violence/women play a role in how shelter workers see themselves. Pattern matching revealed that previous counselling experience rather than age, length of employment and childhood history of trauma had the lowest level of agreement among participants. Findings also suggest that workers with a history of childhood trauma may be more aware of safety issues than workers without a trauma history. recommendations call for more research on shelter workers in Northern Manitoba as well as boards and management of shelters in all parts of the province. Safety issues of workers, organizational values and beliefs of shelters, worker's coping strategies, positive aspects of the profession, and the relationship between worker and client were other areas for future research
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.019 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".