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Record W4378220434 · doi:10.1111/ijsa.12434

Feeling safe at work: Development and validation of the Psychological Safety Inventory

2023· article· en· W4378220434 on OpenAlexafffundabout
Rachel A. Plouffe, Natalie Ein, Jenny J. W. Liu, Kate St. Cyr, Clara Baker, Anthony Nazarov, J. Don Richardson

Bibliographic record

VenueInternational Journal of Selection and Assessment · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsParkwood InstituteMcMaster UniversityPublic Health OntarioSt Joseph's Health CareUniversity of TorontoLawson Health Research InstituteWestern University
FundersCanadian Institute for Military and Veteran Health Research
KeywordsPsychologyPsychological safetyInterpersonal communicationScale (ratio)FeelingReliability (semiconductor)Applied psychologySocial psychologyPerception

Abstract

fetched live from OpenAlex

Abstract Psychological safety, defined as perceptions that an individual within a team is supported and feels safe to take interpersonal risks, voice opinions, and share ideas, is vital for organizational effectiveness. However, there is no consensus on how workplace psychological safety should be measured. We developed the Psychological Safety Inventory (PSI) in response to organizational needs to accurately assess psychological safety. A 70‐item version of the PSI was administered to 497 employees from Canada, the United States, and the United Kingdom. Based on factor analytic findings, we reduced the preliminary PSI to a 30‐item, five‐factor scale. The PSI showed high reliability and correlated as anticipated with convergent measures. Overall, the PSI is a valid and reliable measure of workplace psychological safety.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.147
GPT teacher head0.525
Teacher spread0.377 · 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.

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

Citations18
Published2023
Admission routes3
Has abstractyes

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