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Organizations and Social Worker Well-Being: The Intra-Organizational Context of Practice and Its Impact on a Practitioner's Subjective Well-Being

2013· article· en· W4012335 on OpenAlexaff
Micheal L. Shier, John R. Graham

Bibliographic record

VenueJournal of Health and Human Services Administration · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsContext (archaeology)Social workWell-beingBusinessPsychologyPublic relationsNursingMedicinePolitical sciencePsychotherapist

Abstract

fetched live from OpenAlex

This study aimed to better understand the varied factors that contribute to social worker subjective well-being (SWB) (the social science concept for happiness). Using qualitative methods of inquiry 19 social workers who reported having low to medium levels of workplace and profession satisfaction were interviewed to assess those factors within their lives that they perceived as impacting their well-being. One thematic category from the analysis was aspects of the intraorganizational context of workplaces that can impact social worker SWB. Respondents identified interpersonal workplace relationships, decision-making processes, management/supervisory dynamics, workload and workplace expectations, access to resources and infrastructure support, and inter-organizational relationships as key intra-organizational factors contributing to their overall wellbeing. In conclusion, these findings have practical application within organizations for structured policies and unstructured practices to improve social worker subjective well-being.

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)0.007
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0010.001
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 head0.014
GPT teacher head0.362
Teacher spread0.348 · 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 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

Citations26
Published2013
Admission routes1
Has abstractyes

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