An assessment of professional training for product managers in the pharmaceutical industry
Bibliographic record
Abstract
Purpose The primary purpose of this study is to identify how and where product managers in the pharmaceutical industry receive the training required to undertake their job tasks, and whether or not there is a relationship between the tasks they perform and the training they receive. Design/methodology/approach The methodology for this study was exploratory and descriptive in nature, and utilized a cross‐sectional survey design. Both descriptive and relational statistics are used to analyze the data. Findings The key findings reveal that product managers receive the majority of their training on the job, with the rest supported by company‐sponsored training and outside seminars. Product managers do not appear to receive company training in proportion to the frequency with which particular tasks are performed. Research limitations/implications The limitations to the study are that the findings are limited to one industry and that training needs are self‐reported. Originality/value Managers should not assume that on the job training adequately prepares product managers to do their jobs properly, and training should be an essential part of the product manager's experience. The paper identifies specific areas for future training.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".