A guide to conduct a high-quality survey research
Bibliographic record
Abstract
要約：よく計画された調査研究からは，将来の研究につながるresearch questionなどの重要な情報が得られる。得られた情報は量的なものも質的なものもあり，内容は臨床家の意見から患者による報告まで多岐にわたる。技術の発達により，調査研究が広く容易に実施されるようになったため，普段から調査研究実施の招待を受ける機会や，研究目的のアンケートに招待される機会が増えた。他の研究デザインと同様に，調査研究を批判的吟味する方法を知ることは，結果解釈だけでなく，堅牢な研究実施にも必須である。調査研究は，コストや方法論の点で容易な研究手法と思われがちだが，質の高い研究を実施するのは難題と言える。 したがって，臨床家は調査研究の参加者として，知識の利用者として，研究者として，頑健な方法論を理解する必要がある。本総説では，調査研究の実施や批判的吟味の必須となる事項に関して，研究計画書執筆から論文出版に至るまで記載した。
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 | 0.167 | 0.204 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.085 | 0.072 |
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".