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Record W2418173717 · doi:10.1097/phh.0000000000000331

The Public Health Workforce Interests and Needs Survey

2015· article· en· W2418173717 on OpenAlex
Katie Sellers, Jonathon P. Leider, Elizabeth Harper, Brian C. Castrucci, Kiran Bharthapudi, Rivka Liss‐Levinson, Paul E. Jarris, Edward L. Hunter

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Public Health Management and Practice · 2015
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsnot available
Fundersde Beaumont Foundation
KeywordsWorkforcePublic healthQuarter (Canadian coin)Job satisfactionDemographicsStratified samplingMedicinePsychologyPublic relationsBusinessFamily medicineNursingPolitical scienceDemographyGeographySociologySocial psychology

Abstract

fetched live from OpenAlex

CONTEXT: Public health practitioners, policy makers, and researchers alike have called for more data on individual worker's perceptions about workplace environment, job satisfaction, and training needs for a quarter of a century. The Public Health Workforce Interests and Needs Survey (PH WINS) was created to answer that call. OBJECTIVE: Characterize key components of the public health workforce, including demographics, workplace environment, perceptions about national trends, and perceived training needs. DESIGN: A nationally representative survey of central office employees at state health agencies (SHAs) was conducted in 2014. Approximately 25,000 e-mail invitations to a Web-based survey were sent out to public health staff in 37 states, based on a stratified sampling approach. Balanced repeated replication weights were used to account for the complex sampling design. SETTING AND PARTICIPANTS: A total of 10,246 permanently employed SHA central office employees participated in PH WINS (46% response rate). MAIN OUTCOME MEASURES: Perceptions about training needs; workplace environment and job satisfaction; national initiatives and trends; and demographics. RESULTS: Although the majority of staff said they were somewhat or very satisfied with their job (79%; 95% confidence interval [CI], 78-80), as well as their organization (65%; 95% CI, 64-66), more than 42% (95% CI, 41-43) were considering leaving their organization in the next year or retiring before 2020; 4% of those were considering leaving for another job elsewhere in governmental public health. The majority of public health staff at SHA central offices are female (72%; 95% CI, 71-73), non-Hispanic white (70%; 95% CI, 69-71), and older than 40 years (73%; 95% CI, 72-74). The greatest training needs include influencing policy development, preparing a budget, and training related to the social determinants of health. CONCLUSIONS: PH WINS represents the first nationally representative survey of SHA employees. It holds significant potential to help answer previously unaddressed questions in public health workforce research and provides actionable findings for SHA leaders.

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.

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)0.090
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.690
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0900.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.474
GPT teacher head0.551
Teacher spread0.077 · 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