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Record W4207082025 · doi:10.3138/jmvfh-2021-0075

Scoping review of mentorship programs for women in the military

2022· article· en· W4207082025 on OpenAlexaffvenue
Linna Tam‐Seto, Bibi Imre‐Millei

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2022
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsQueen's University
Fundersnot available
KeywordsMentorshipAffect (linguistics)Medical educationMilitary personnelIdentity (music)MedicinePolitical sciencePsychology

Abstract

fetched live from OpenAlex

LAY SUMMARY Mentorship is used in a variety of professions, including in the military to support careers and personal development. Mentorship is known to have benefits for mentors, mentees, and organizations, and it is being used more often to support minority populations, such as women in certain male-dominated professions. However, little is known about how gender and cultures within the military affect mentoring and mentorship programs. A review of the literature was completed to answer the question “How does the literature conceptualize and describe mentorship and mentoring programs in military, military adjacent, and policing organizations for women?” A total of 23 articles were included, the majority of which were from the United States. These articles discussed defining mentorship, how mentorship is used, the role of identity, and the impact of culture on mentorship. This review highlights gaps in the current body of research, calling on the need for further exploration.

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)0.010
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.045
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0120.013
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.091
GPT teacher head0.388
Teacher spread0.297 · 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 designSystematic review
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

Citations3
Published2022
Admission routes2
Has abstractyes

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