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Record W2065421794 · doi:10.2196/jmir.6.1.e11

Pharmacist Computer Skills and Needs Assessment Survey

2004· article· en· W2065421794 on OpenAlexaffabout
Robert M. Balen, Peter J. Jewesson

Bibliographic record

VenueJournal of Medical Internet Research · 2004
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsVancouver General Hospital
Fundersnot available
KeywordsComputer literacyPharmacyInformaticsPharmacistMedical educationPresentation (obstetrics)The InternetClinical pharmacyHealth informaticsMedicineNursingComputer scienceWorld Wide WebPublic health

Abstract

fetched live from OpenAlex

BACKGROUND: To use technology effectively for the advancement of patient care, pharmacists must possess a variety of computer skills. We recently introduced a novel applied informatics program in this Canadian hospital clinical service unit to enhance the informatics skills of our members. OBJECTIVE: This study was conducted to gain a better understanding of the baseline computer skills and needs of our hospital pharmacists immediately prior to the implementation of an applied informatics program. METHODS: In May 2001, an 84-question written survey was distributed by mail to 106 practicing hospital pharmacists in our multi-site, 1500-bed, acute-adult-tertiary care Canadian teaching hospital in Vancouver, British Columbia. RESULTS: Fifty-eight surveys (55% of total) were returned within the two-week study period. The survey responses reflected the opinions of licensed BSc and PharmD hospital pharmacists with a broad range of pharmacy practice experience. Most respondents had home access to personal computers, and regularly used computers in the work environment for drug distribution, information management, and communication purposes. Few respondents reported experience with handheld computers. Software use experience varied according to application. Although patient-care information software and e-mail were commonly used, experience with spreadsheet, statistical, and presentation software was negligible. The respondents were familiar with Internet search engines, and these were reported to be the most common method of seeking clinical information online. Although many respondents rated themselves as being generally computer literate and not particularly anxious about using computers, the majority believed they required more training to reach their desired level of computer literacy. Lack of familiarity with computer-related terms was prevalent. Self-reported basic computer skill was typically at a moderate level, and varied depending on the task. Specifically, respondents rated their ability to manipulate files, use software help features, and install software as low, but rated their ability to access and navigate the Internet as high. Respondents were generally aware of what online resources were available to them and Clinical Pharmacology was the most commonly employed reference. In terms of anticipated needs, most pharmacists believed they needed to upgrade their computer skills. Medical database and Internet searching skills were identified as those in greatest need of improvement. CONCLUSIONS: Most pharmacists believed they needed to upgrade their computer skills. Medical database and Internet searching skills were identified as those in greatest need of improvement for the purposes of improving practice effectiveness.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.215
GPT teacher head0.613
Teacher spread0.399 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations40
Published2004
Admission routes2
Has abstractyes

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