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Record W6995392808

Organizational context, healthcare reforms and professional distress in Canadian social workers: understanding the epidemic

2020· article· en· W6995392808 on OpenAlexaboutno aff

Bibliographic record

VenueIRIS Research product catalog (Sapienza University of Rome) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsnot available
Fundersnot available
KeywordsRestructuringHealth careThematic analysisSocial workIdeologyAffect (linguistics)DistressOdds
DOInot available

Abstract

fetched live from OpenAlex

This study investigated the social experience of social workers in a healthcare setting. While we know that organizational constraints and structural reforms affect the workplace well-being of social workers, how these changes are received by front-line practitioners is unclear. To deepen our understanding of this issue, we conducted a thematic analysis of thirty semi-directed interviews with social workers currently practicing in three Canadian cities (Ottawa, Moncton and Winnipeg). Discussions of daily work life, responsibilities, limitations and subjective appreciation of the social worker’s role revealed which organizational constraints were the most significant in everyday practice and how these constraints relate to identity and mandate. Healthcare reforms were found to be generally negative for social workers, whose struggles for recognition were impaired by the fundamentally neoliberal ideologies behind large-scale restructuring at odds with the humanistic principles of social work. This investigation highlights the importance of organizational improvements of the workplace through systemic changes targeting managerial expectations, resources allocation, work life balance and the respect of professional values concurrently.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.697

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.006
Science and technology studies0.0200.011
Scholarly communication0.0080.004
Open science0.0020.009
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.137
GPT teacher head0.394
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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