Working Conditions of Front-Line Poverty-Reduction Staff at Non-profit Agencies
Bibliographic record
Abstract
Over the past three to four decades in Ontario, neoliberalization and new public management \nhave restructured the non-profit social services (NPSS) sector by reducing core funding and \nintroducing a competitive proposal system with increased managerial accountability. These \nchanges have generated immense workplace pressures for frontline staff. Frontline staff in the \nNPSS have seen an increase in standardization accompanied by the degradation of their skills. \nThrough in-depth interviews with five frontline staff at two similar non-profit agencies serving \npeople experiencing poverty in the Niagara Region, this paper explores the question: How do \nfrontline staff in the non-profit social services sector describe their working conditions? And \nhow resonant are the narratives of compassion fatigue and burnout. In contrast to the narrative of \n"compassion fatigue" that often describes the experiences of professional frontline workers, I \nfound that burnout among frontline poverty-reduction staff stems primarily from encountering \nstructural barriers, such as a lack of affordable housing, that limit what they can do to help their \nservice users. Furthermore, I found a general lack of organizational supports for frontline staff as \nworkers, including supports to prevent or lessen burnout. This research brings to light new \nperspectives regarding poverty-reduction work and ultimately points to needed supports for \nfrontline staff that may improve their work lives, well-being and poverty-reduction effectiveness.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".