The job of a performance consultant: a qualitative content analysis of job descriptions
Bibliographic record
Abstract
Purpose – This study aims to explore the competencies needed by performance consultants, a particular role identified for training and development professionals. The role was formally named and promoted nearly two decades ago. Two ongoing discussions in the field are the competencies needed by training and development professionals and the role of consulting within the field. Design/methodology/approach – This study identifies the general competencies needed by a performance consultant as reflected in job descriptions for the position. It accomplished this goal by collecting job descriptions for the position from organizations in Canada (the result of a practical arrangement with an organization that would collect the descriptions and remove identifying information before the research team analyzed them), systematically analyzing them using qualitative content analysis techniques and generating a profile of the position, which can be used as a basis for further analysis of the position. Findings – The job title and competencies sought in the job descriptions differ from those proposed in the literature. Specific areas of difference include the title (none of the job descriptions analyzed explicitly used the title performance consultant), role in needs analysis and client relationships, technology competence (the job descriptions sought little, if any, while the literature suggests broad conceptual knowledge) and qualifications (most job descriptions only require a bachelor’s degree; many training and development professionals have more education). Research limitations/implications – The profile presented in this paper only represents that used in job descriptions (typically an idealized version) and in a particular national context. But if the results are validated with other methodologies and in other contexts, they suggest that the actual consulting role significantly differs from the one conceptualized in the literature. Practical implications – The findings suggest that the consultant role conceived in the literature differs from the actual job expected by employers, at least as reflected in job descriptions. Research with incumbents in the job is needed to assess whether the inconsistencies are also reflected in the day-to-day work. Social implications – Social implications validate the broad concern that trainers have skills and talents to offer organizations that those organizations do not fully utilize. Originality/value – The paper provides one of the few empirical studies of the job responsibilities of a performance consultant.
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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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".