How to formulate high-quality lessons learned: a rapid review
Bibliographic record
Abstract
appears in the titles of thousands of scientific articles, most do not describe how these lessons were produced or the level of rigor involved in their development. As part of a project aimed at deriving lessons from hospitals' resilience during the COVID-19 pandemic in five countries (the HoSPiCOVID project), we sought to systematised the process of producing these lessons. To do so, we conducted a rapid review to identify the best ways of developing quality lessons learned (QLLs). A QLL results from a systematic process of collecting, compiling, and analysing data derived from a research project. The rapid review follows the same key steps as a systematic review, adapted to a more accelerated and pragmatic format. From 1,881 documents initially identified, 18 were retained. Their analysis identified three principles to guide the process of developing QLLs: 1) Creating a supportive climate; 2) Choosing the right leaders or facilitators for the process; and 3) Engaging in a scientific approach. Based on these findings, we developed a guide comprising 11 steps, structured into two main phases: preparatory steps for QLL development, and steps for identifying and formulating QLLs. This guide offers a structured process for teams seeking to enhance the rigor, clarity, and potential transferability of the lessons they formulate.
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 | 0.374 | 0.642 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.029 | 0.022 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.023 | 0.033 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".