A User Survey Finds That a Hospital Library Literature Search Service Has a Direct Impact on Patient Care
Bibliographic record
Abstract
A Review of: Farrell, A., Mason, J. (2014). Evaluating the Impact of Literature Searching Services on Patient Care Through the Use of a Quick-Assessment Tool. Journal of the Canadian Health Libraries Association, 35(3),116-123. doi: 10.5596/c14-030 Abstract Objective – To assess the impact of a library provided literature search service on patient care. Design – Multiple choice questionnaire survey. Setting – Hospital library. Subjects – 54 library users who had requested a literature search and indicated the primary purpose of their request was patient care. Methods – A multiple choice questionnaire survey was designed, building on previously published library impact surveys and best practice guidelines, with input from staff in the local research department. The survey was reviewed by library staff, researchers and prospective respondents and piloted. The survey was sent out with the answers to literature search requests and a small incentive was offered to those who completed the survey. The survey was followed up with reminders. Main results – The response rate was 57.5% (n=54/94). The most common staff groups requesting literature searches were physicians (33.3%), nurses (22.2%), therapists (16.7%), pharmacists (11.1%) and residents (7.4%). The majority stated that their questions had been answered (77.8%), while 18.5% indicated their questions had been partially answered, for reasons such as the answer leading to more questions, or parts of the question had not been addressed. Two (3.7%) of the respondents’ questions were not answered, either because no answer existed, or because the question didn’t contain enough detail. Of those who replied that their question had been answered, 64.3% judged the information to have had an immediate impact on patient treatment or management. Other uses of the information included refreshing memory, avoiding an adverse event, diagnosing a patient, or preventing a referral. The percentage of respondents judging there to be no immediate impact on patient care was 16.7%. The impact on diagnosis and treatment was further investigated in those who had said there had been an immediate impact, with 22.2% saying the information determined their choice of drug, 29.6% saying the information confirmed their choice of drug, while 18.5% stated the information changed their choice of drug. All respondents replied that they intended to use this information in the future, regardless of whether the information had an immediate impact, or if their question had been answered. Conclusion – The authors concluded that the survey results show hospital libraries can have an impact on patient care through a literature searching service. They also found that the library was answering its literature service users’ questions.
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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 | 0.038 | 0.273 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".