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Record W3198280256 · doi:10.1080/23303131.2021.1967245

Organizational Change in Human Service Organizations: A Review and Content Analysis

2021· review· en· W3198280256 on OpenAlexafffundabout
John R. Graham, Kyler Woodmass, Quinn Bailey, Eric Ping Hung Li, Arielle Lomness

Bibliographic record

VenueHuman Services Organizations Management Leadership & Governance · 2021
Typereview
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsContent analysisOrganizational changeHuman servicesService (business)Knowledge managementBusinessProcess managementPolitical sciencePublic relationsSociologyComputer scienceMarketingSocial science

Abstract

fetched live from OpenAlex

This literature review examines organizational change scholarship within human service organizations (“health,” “human,” and “social” services) between 1968 and 2020. MEDLINE, CINAHL, Social Work Abstracts, EMBASE, and Sociology Collection databases were searched for peer-reviewed, English-language items. The vast majority of first authors were based in the UK, the US, Australia, and Canada, though UK-based authors produced over a third of included items. Forty-two journals had multiple included items. Four main, interconnected themes were identified and discussed: external rationales for change (e.g. adopting evidence-based practices, structural shifts, community demands); type and level of change (e.g. frontline interventions, restructuring management, improving internal and external relations); implementing changes (targeted interventions, broader implementation models, and successful “tactics”); and internal characteristics that both promote and inhibit change (leadership, readiness, communication, learning orientation, skills). Growing scholarship warrants frequent review and summarization for diverse actors.

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)0.011
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.026
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0260.034
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0010.001
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 head0.179
GPT teacher head0.368
Teacher spread0.189 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations15
Published2021
Admission routes3
Has abstractyes

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