Mentoring experiences of successful women across the Americas
Bibliographic record
Abstract
Purpose The purpose of this paper is to compare women's mentoring experience in nine countries within the Americas, and to explore linkages between personal characteristics, mentoring practices, mentoring functions, and consequences of being mentee. Design/methodology/approach A total of 1,146 successful women are questioned about their mentoring experiences as a mentee: 105 from Argentina, 210 from Brazil, 199 from Canada, 84 from Chile, 232 from Mexico, 126 from the USA, and 190 from three countries in the West Indies (Barbados, Jamaica, and St Vincent). Findings Most of the women have more than one mentor. Across all countries mentoring practices are more strongly linked to career mentoring function while the age and gender of the mentor are more strongly linked to psychosocial mentoring. Mentoring from the perspective of mentee has the same directional relationship with situational and individual variables, but the significance of those relationships vary by country. A possible cultural difference is detected between Spanish and non‐Spanish speaking countries on the issue of mentoring practice. Research limitations/implications The fact that the paper focuses only on successful women in this paper means the findings are not necessarily generalizable to other groups of women or men. The paper is also limited because mentoring functions are constrained to two: psychosocial and career. There may be more functions that mentoring could fulfill for the mentee. Practical implications Companies' interest in fostering mentoring among their members, particularly women, should be aware that different mentoring functions are influenced by different factors. For example, formal mentoring programs appear to have a greater impact on career mentoring functions than on psychosocial mentoring functions. To support women in their careers, companies should institute formal mentoring programs; this is especially important in South American countries. Moreover, mentoring programs must be designed to be adaptive since the analyses indicated that there are significant differences by country in terms of many mentoring issues. Originality/value In the literature review, the paper finds linkages between culture, mentoring practices, characteristics of mentors and mentees, and mentoring functions, but no evidence that these linkages have been studied with a group of professionally successful women from different American countries, particularly non‐English speaking American countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".