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Record W4235120670 · doi:10.4018/9781605663562.ch041

Social Marketing in Healthcare

2011· book-chapter· en· W4235120670 on OpenAlexaff
Manuel W. Mah

Bibliographic record

VenueIGI Global eBooks · 2011
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDatabase transactionBusinessMarketingSocial mediaMarketing researchMarketing mixCreativityDigital marketingSocial marketingInfluencer marketingThe InternetRelationship marketingAdvertisingComputer scienceMarketing managementWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

Social marketing is a way to influence the behaviors of stakeholders in the healthcare system. This chapter will define the traditional transaction marketing concepts of exchange, segmentation, competition, the marketing mix, and audience orientation. Then it will describe the current paradigm shift to relationship marketing with its logic of collaboration and the cocreation of value. Relationship marketing is enhanced by the arrival of Internet-based “social media” such as blogs, file sharing sites, and social networking sites that place creativity and communication channels under “audience” control. These developments in marketing strategy and social software will profoundly affect the next generation of social marketing programs.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.894
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.040
GPT teacher head0.256
Teacher spread0.216 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations3
Published2011
Admission routes1
Has abstractyes

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