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Record W2051188965 · doi:10.1097/nnr.0000000000000052

Point-of-Care Research

2014· editorial· en· W2051188965 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueNursing Research · 2014
Typeeditorial
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsPoint (geometry)Health careIntersection (aeronautics)Point of careMobile deviceAction (physics)Everyday lifePoliticsInternet privacyPsychologyNursingPublic relationsMedicineComputer sciencePolitical scienceWorld Wide WebEngineeringLaw

Abstract

fetched live from OpenAlex

What images come to mind when you hear the phrase “point of care”? Do you visualize a nurse advising a mother about her child’s minor ailment in the clinic or a nurse helping a patient get out of bed in the ICU? Whatever you envision, some intersection of patient, nurse, place, and time is likely part of the picture. In the past, the point of care was taken to be a setting—hospital, clinic, infirmary—designed specifically to provide health services at specific times by specific providers to specific people with specific conditions or concerns in specific ways. Now, radical changes in technology and information, consumer expectations, demographics, health economics, and political action are revolutionizing the way care is provided. This perfect storm is upending conventional notions about point of care and opening new opportunities and challenges for nursing research. The incredible expansion of mobile cellular subscriptions—projected to reach almost 7 billion by the end of this year (International Telecommunication Union, 2014)—may be the most significant factor allowing the point of care to extend from traditional settings to the point of living, wherever it may be. First smart handheld devices and now computers incorporated into the mundane stuff of everyday life like t-shirts have allowed generation of the quantified self-movement (Wolf, 2010). Self-collection of personal health data on-the-go was coemergent with ideas for novel patient-driven healthcare models (Swan, 2009), the potential of which has been little explored or utilized. The same handheld, interconnected, smart technology generated the move for m-health that allows “point of care in your pocket” for healthcare providers (van Heerden, Tomlinson, & Swartz, 2012). Try a quick search of MEDLINE using “point of care” as keyword; in over 10,000 hits, you’ll find that lab testing and use of devices at the bedside in hospitals, in long-term care units, in homes, and in the field are fast changing the way assessments are done and treatment decisions are made (Bier & Schumacher, 2013; Walia, 2013). Telehealth is extending the reach of place-bound providers to the technology-mediated point of care (Institute of Medicine, 2012). Evaluation of quality in point-of-care testing and telehealth interactions is needed, however. Wireless technology and the Internet of things (IEEE Standards Association, 2014) are creating smart environments for the point of care. Smart, patient-centered ICUs designed for healing and capitalizing on information provided by sensors and devices are envisioned (Halpern, 2014). Sensor and information capabilities have potential to inform on-going adaptation of assistive technology to enhance independence for those aging with disabilities (Agree, 2014). Safety is a critical component of point-of-care research in nursing. For example, barcode technology and information technology are used in nursing units, laboratories, and pharmacies of hospitals around the world to support safe medication administration and accurate handling of specimens (e.g., Agrawal, 2009; Miller, Akers, Magrin, Whitehead, & Davis, 2013). Still, challenges in implementation exist (Voshall, Piscotty, Lawrence, & Targosz, 2013), and research is needed to ensure that the best systems are developed, deployed, used properly, and used for system improvement. Infusion of technology, the possibly intrusive nature of information gathering, and privacy concerns raise questions about human factors for point-of-care research. Information technology has potential to change the nursing process at the point of care (Courtney, Demiris, & Alexander, 2005) and preservation of the caring environment amidst the technology at point of care is an ongoing concern (Buckner & Gregory, 2011). Sensors allowing real-time monitoring for safety may support independence for elderly people at home, but more knowledge is needed about the attitudes, acceptability, and rated usefulness of the systems (Cesta et al., 2011). The increase in funding opportunities for point-of-care research underscores the expectation that new knowledge is needed to understand what works at the point of care, for whom, where, and when. The National Institute of Biomedical Imaging and Bioengineering (n.d.) sponsors the Point-of-Care Technologies Research Network; the National Institute of Nursing Research (n.d.) asked about how point-of-care/self-monitoring diagnostic devices could significantly improve self-management to improve quality of life for individuals with chronic illness. A search for “point-of-care” on the Agency for Healthcare Research and Quality Web site returned almost 1,500 results across their research portfolios. The Bill and Melinda Gates Foundation and Grand Challenges Canada have partnered to fund innovative ideas for point-of-care diagnostics in the developing world (“Foundation and Grand Challenges Canada,” n.d.). Papers considered for the new, ongoing series should report findings from original point-of-care research studies. Topics include but are not limited to use of devices and information technology at the point of care, patient safety issues, m-health, telehealth, and system interoperability. Design and evaluation of “smart” environments across the health–illness continuum and research about learning health systems are relevant to the call. Findings from investigations of communication and decision-making in emerging technology-supported point-of-care settings are welcome. Papers may be enhanced to include video or interactive graphs using supplemental digital content. In advance of submission, queries to the Editor are encouraged but not required. Submissions may be regular full-length papers or research briefs. In the letter to the editor uploaded with submissions, please mention that the paper should be considered for the series. Consider point-of-care research. Contribute to new knowledge about the point of care in our digital, interconnected world.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.048
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.129
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.048
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0050.002
Scholarly communication0.0000.000
Open science0.0040.001
Research integrity0.0040.027
Insufficient payload (model declined to judge)0.0000.002

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 head0.500
GPT teacher head0.638
Teacher spread0.137 · 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