Bibliographic record
Abstract
Purpose The purpose of this paper is to document the ways pharmaceutical representatives learn for work and report attributes of (in)formality and other characteristics of ways of learning perceived as effective and frequently used. Design/methodology/approach A total of agents 20 from 11 pharmaceutical manufacturers across Canada participated in a Delphi collaboration creating a comprehensive list of ways in which they learn for work. In‐depth individual interviews with four agents explored the ways of learning they perceived as most frequent and effective. The Colley et al. framework was interpreted, extended, and applied to identify attributes of (in)formality and other elemental characteristics of these ways of learning. Findings Agents in this rapidly changing, competitive industry worked alone in geographically distributed territories. Learning had a special role in this industry: agents developed themselves broadly as resources to gain customer‐access required to promote products. Delphi participants identified 64 ways of learning (five categories). Most ways were self‐initiated, self‐directed, minimally structured, and may involve intentional incidental learning. Reported frequent and effective ways differed by agent, but all reported frequent and effective learning through self‐directed means with mixed (in)formal attributes. Customer facilitated and peer‐facilitated learning were common, despite isolation from co‐workers. Originality/value This paper reports on learning in a distinct and under‐researched industry. It demonstrates the importance of peer‐facilitated and on‐the‐job learning even in a distributed workforce and documents intentional incidental learning. It discovers an indirect way in which learning supports business objectives and it provides a framing tool for guiding reporting of characteristics of ways of learning.
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 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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".