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Record W198533144

Burnout and job satisfaction among frontline child protection workers: A departmental analysis.

2005· article· en· W198533144 on OpenAlexaboutno aff
Rachelle. Rail

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsnot available
FundersDivision of Graduate Education
KeywordsBurnoutJob satisfactionPsychologyNursingBusinessSocial psychologyMedicineClinical psychology
DOInot available

Abstract

fetched live from OpenAlex

The present study explored the levels of burnout and job satisfaction experienced by frontline child protection practitioners and whether the department in which they worked was associated with burnout and job satisfaction. Using a review of the research, as well as quantitative and qualitative methods, this study examined the prevalence and correlates of burnout and job satisfaction and proposed interventions for addressing these issues among frontline child protection workers. A survey that collected information on levels of burnout and job satisfaction among direct service child protection workers within the generally accepted frontline departments was administered to 112 frontline child protection workers. The Maslach Burnout Inventory (MBI) was used to measure burnout on three dimensions: emotional exhaustion (EE), depersonalization (DP) and personal accomplishment (PA) while job satisfaction was measured by both the single-item and full measure of the job satisfaction scale found in the Quality of Employment survey by Quinn and his colleagues. (Abstract shortened by UMI.) Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .R35. Source: Masters Abstracts International, Volume: 44-03, page: 1234. Thesis (M.S.W.)--University of Windsor (Canada), 2005.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.272
Teacher spread0.253 · 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 teacher head, not a consensus.

Study designObservational
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

Citations2
Published2005
Admission routes1
Has abstractyes

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